Hey guys, it's Nate, A.K. The Foot Account, and welcome back to the channel. After a busy day of wants to watch day one, we are gonna talk about day two, this day of Saturday, and the team of the week, Untradable Pack, that I think is coming today. I'm gonna talk about why I think it's coming, of course, looking at the deadline day, webpage from EA Sports, what impact that could have on the market, and of course, cover everything that happened yesterday with the wants to watch market movements, cards growing up and down, the pack supply, and the market actually starting to rise now on some of those cards that we bought two days ago, and even earlier last night, is going as it was supposed to be planned. So let's take a look at this deadline day. The countdown here is towards a different kind of time. It's a weird time frame. 9 a.m. U.K. time is, I think, when this counts down towards, which should be the time, it's like the cutoff and the deadline, for if you get on FIFA 21 before that timeframe, you will be eligible for this pack right here, the deadline day pack, which guarantees you some untradable team of the weak players and some lone wants to watch items, but that team of the weak player is what we're most looking forward to again today. And this is gonna be coming today, I believe, because it is expiring at that 11.59 Pacific time on October 9th, which is this countdown right here. So I do believe that this pack would actually come today.  So I do expect this to come out today. Does that affect the market? I do think it will, because think about everybody that has FIFA right now is everybody that's pre-ordered, got the game today from a physical copy in a store, or everybody that was on the game from EA Access or Early Access, and that's a lot of players. That's a lot of people on this market, just in total, the number of live transfers that were on foot today in general, jumped from around, I think it was around seven to eight million at its peak on PlayStation, and that was a jump from around five million where it was the past couple of days during Early Access. So a lot of new guys got on the game today, and that also affect the market. We'll talk about that in a second, but I also think that just means that a lot of people are gonna get this wants to watch, or excuse me, Team of the Week player pack, and that's gonna affect some of these prices. Now these Team of the Week cards are down. After all the supply coming from today, it looks like they dropped down a little bit. Of course, we're looking at popular informs like Alan St. Maximon. He was bouncing around between 2.20 this morning, all the way up to like 2.60, 2.55 right here, and then back down to 2.30, and now he's chilling around that 2.30 range still. Same thing with Highland, he was up at 2.60 today. Again, back down to 2.30. Levandosky was at 3.30 at one point. He's at 3.00. Taylor Hernandez at 88,000 coins. Ali Watkins and Homage Rodriguez both were extinct during the first day when everybody was investing in them and sniping them. Now they're basically back down to discards. It's kind of crazy the fluctuations with this Team of the Week with a lot of the OP players that are in it, and it looks like they're still gonna stay pretty low. I would expect them to rise out of the weekend historically in past FIFA's. Saturday has been like one of the cheapest days to invest in Team of the Week cards. Now if you bought these yesterday, they're basically, if you bought them at a low price yesterday on Friday, which is when I thought they were gonna be their lowest off of the bat, it looks like they're still gonna be pretty low on today on Saturday, and maybe a bit of a drop right away at 6 p.m. if we do get this deadline day pack, and everybody goes and gets that trade or that untradable Team of the Week player. So that could be something if you're wanting to invest in Team of the Week too, that we could be seeing those and seeing a little bit of panic selling on those cards possibly today if that pack does come out. So we'll wait for 6 p.m. UK for that today. One other thing that could be coming at 6 p.m. is the LaLiga Player of the Month card. Now Footwatch tweeted this out a couple hours ago and they said EA updated the code with a LaLiga POTM card. It shows an embargo time of Saturday 5 p.m. GMT, so the SBC should be at six. So hopefully we're gonna be seeing LaLiga's Player of the Month Ansu Fati. Looks like maybe a little concept card here down below the tweet.  Of course, we've been kind of not fearing that this card was coming, but especially for that Ansu Fati inform himself, a lot of people wanna get in on that card and use that card, cause he's very meta, but that could be another big time SBC that would come out today and affect the market. Now, I feel like they would price this differently and they would price it cheaper than what they did with the dominant Calvert Lewin SBC because again, that Player of the Month SBC is Premier League and a lot of the Premier League stuff, SBCs and other content that gets released in this game is just overpriced because EA know they can suck more coins off the market as more people will do that type of SBCs so they price it higher cause there's more demand for that stuff. Now, Ansu Fati SBC would be very popular as well. What kind of stuff would this require? They already required an inform in the first SBC of the year. They're already requiring informs for the dominant Calvert Lewin SBC. I would be not surprised, again, if they required more informs for Ansu Fati. Hopefully it's just one squad for Ansu Fati, probably 83 rated. I don't expect 83 rated cards, especially informs, to rise in the market. Possibly we could see a few 83s rise if there's more demand. Nothing really rose for the dominant Calvert Lewin SBC cause it was just overpriced at 65K. There's just not enough demand there at that price point for people to go and do this SBC. So if you wanna take a look again, if you have some discard 82s or 83s that you wanna pop into, buy some of those cards, that's not a bad shout. 84s actually got cheaper today. All this SBC fodder, the higher rated stuff, there's just no demand for it. There's no SBCs that are out right now. All this stuff keeps going lower. It's still at 3,000 coins or yards of ball at 3.5K. If there's any La Liga requirements or Spanish requirements or Ansu Fati, that could be a card that goes up. And again, this stuff is all like really, really low risk buys right now. If you wanna do a club stock, I would recommend doing some of that because these cards are just very cheap. And we're probably gonna get other SBCs during this promo and in the weeks ahead with the ultimate screen promo coming out and other SBCs that EA might start dropping like flashbacks and stuff like that. It's not a bad time to do a club stock if you do have the coins at the moment. But let's talk about the rest of the market because we're just banking on this market rise. We have a lot of coins invested in meta players on this market, which is what we've been doing the past two days. And one thing I wanna start talking about first is foot bin. Foot bin has been messing up some of these prices. I wanted to kind of let you guys know about that.  It seems to be that foot bin is catching some of these undercuts that people are listing up with all these packs being opened, all the supply coming out. And people also panic selling at the same time. You're seeing undercuts and foot bin is somehow catching those like they didn't in years past. I don't know if they have like an average in years past but foot bin is catching these undercuts. Right now, sun on foot bin shows as 255 but he's actually 280 on the market. So this can be used to your advantage if you're trying to snipe a player where you could see him get listed at the foot bin price instead of what he's actually going for on the market and that could actually make you some coins. So maybe this is like a new FIFA 21 trading method is like the foot bin method where we look for cards on foot bin that have price points that are not accurate to what they actually are in the game. But just be careful of this. When you're looking on foot bin to go and sell a card or to go and buy a card, you don't always just go by what foot bin says or what foot whiz says. Actually go on the market and check that card out and search it for yourself because that's gonna get the true price. You're gonna find that true price. And in this case, you'd be saving yourself 25, 30,000 coins if you pack this sun and listed them at foot bin price, you would be not getting all the coins that you could actually get for this card. So just a little bit of a tip there but a lot of these foot bin prices are showing that the market is rising back up which is what we expected with all of the pack supply. A lot of the lower tier cards are kind of rising up quicker, right? Your Rente was like, I think 17,000 coins earlier today. And a lot of the lower tier guys, I was buying Coutinho's today at around 8,000 coins. He's selling, he rose up. You can see there was a lot of dip here during all the supply. He was 21,000 coins, went down to 18 and now he's back up at 21K. I think this is just attributed to a lot of people that are getting on the game for the first time. They still don't have a lot of coins. So they're buying a lot of these players that are in the lower tier range. That's maybe why you didn't see as much of a rise on guys like Dembele, Joe Gomez, Ferlan Mendi, Ed and Hazard. Now while some of those players did rise, Rashford's up a bit, Pogba's up a bit, I think those cards are gonna more gradually rise into the next week as people graduate from a card like a Marcus Jarente and move into something a little bit more expensive.  That could be something that you see but it looks like the biggest movers of the, at least the last, you know, 10 to 12 hours have been those lower meta, lower rated meta cards. Joe Felix, I was buying today on stream sniping for 13,000 coins. He's up to 15.5, Porto is at 7K, he's now at 8.8. Even Domins and Sanchez, just with the lack of players in the Premier League at center back, he has risen, Fakir is up and just a lot of these cards did bounce back a decent amount. Now, if you're somebody who invested with me or with a lot of other guys in this game that were shouting high rated meta players and you bought those for your team, that was the best buys in my opinion. A lot of these cards are still chilling around similar prices. I still have Fabinho's, Joe Gomez, Jadon Sancho and Alaba still sitting on my transfer list as well. I still have a Davies. Davies is kind of, you know, still chilling around that 60K price range. I would continue to hold on to these cards because just throughout the next week, as long as we don't get a repeatable sort of SPC from EA Sports, I don't think there's anything that's really gonna drag this market down. It's just people are upgrading their teams, they're getting their division rivals placement coins probably tonight into tomorrow and that's just gonna make the market boom even more as they get more coins. Some people getting, you know, 60, 70,000, maybe even 120,000 coins for getting Div 2 and that stuff is just going to continue to keep going up and up and up. So that's kinda how I'm feeling about the market right now. Again, just be careful today. I really feel like if we're gonna get any sort of content I wouldn't expect another SPC once-to-watch player from EA Sports. I know we have the objective going on and we have the SPC for Tiago Silva today. I really just feel like it's gonna be that deadline day pack coming out today, a possible Anzufati and then probably not another objective as well. Maybe a pack supply SPC, but again, that would just put more coins on the market. So continue to watch the market rise. I know there's no week in league. If there was week in league, we would see a lot more higher of a rise and a lot quicker of a rise, but that is next week to worry about. So a lot of these cards that we have invested in as well, you can kind of set a timeframe. If you wanna just get them out, maybe for next Thursday with week in league rewards or some of these high-rated meta cards, that could be kind of like a target sell time for some of those. And we honestly should be watching the market rise all week long. Now, if you wanna do some quick flips and trading on the market still, you can absolutely do that with position change trading, with chemistry style trading. There are a lot of cards that people are trying to fit in their teams right now. Raja9 Golan has a 83-rated card on this game. I think is right around 20K. But he sells for a decent amount more with a center defensive mid position change or with the shadow chemistry style applied because his card in game actually fits that type of position better. So he's a center attacking mid by base position. You move that to center defensive mid and he's actually going for around, is it 5,000 coins more? 6,000 coins more. Wow, if this actually sells at 26K, that'd be kind of crazy. That's almost a 10,000 coin boost up in price from where he is with just his normal item. Let's see if I can maybe snag a quick snipe who are listing under 20,000 coins. Because people are changing their teams around all the time and upgrading, somebody might be listing their nine goal in as a CDM. And if he's actually selling an even 24,000 coins, this could be a really nice filter that you could sit on. But there's a lot of other players like this in the meantime, if you're waiting for an investments to rise and you have some coins at the ready and handy in the top left of your screen, in your coin balance, then you could definitely do some sort of this trading because people are still upgrading their teams and they're still figuring out how they can progress inside of FIFA 21 Ultimate Team and take their team to the top. So again, quick recap, just be careful, the deadline day pack today could cause some untradable packs to be open and some maybe panic selling as people track, people pack an untradable player from the team of the week and they go and sell the tradable one that they have in their squad. Just be careful of that. And the Anzufati SBC that possibly could be coming today as well. Don't think it's gonna be too expensive but there'd be a lot of hype for that card. Link investments as well. You think about other Barcelona midfielders. Maybe a guy like Griezmann, who isn't up that much today, could be a card that would rise if this SBC would come out and other center mids in the La Liga or Barcelona. Maybe again, like a Galicutino or left backs, like a Jordi Alba. Somebody like that could be rising on the market today as a link investment to that Anzufati depending on the price, depending on the rating upgrade. And of course, if enough hype is there for a card like that, he has met us. So there's gonna be hype there. It just really depends on price and on the actual stats of the card. So if you enjoyed this video, smash the thumbs up on it, comment down below if you have any questions and subscribe to the channel for new. It's been Nate, the Foot Account and catch you guys later. Peace out.


It's time for the Lawn Jean Chronoscope, a television journal of the important issues of the hour brought to you every Monday, Wednesday, and Friday, a presentation of the Lawn Jean Wittner Watch Company, maker of Lawn Jean, the world's most honored watch, and Wittner, distinguished companion to the world-honored Lawn Jean. Good evening. This is Frank Knight. May I introduce our co-editors for this edition of the Lawn Jean Chronoscope? Larry Lasser from the CBS television news staff and Krishna Balraman, foreign correspondent for the Indian newspaper The Hindu. Our distinguished guest for this evening is the Honorable Chester Bowles, former ambassador to India. Mr. Bowles, you've had a lot of experience as an American businessman and in government service before you became ambassador to India. Could you tell us just how important do you think India is to the United States as a friendly power? Well, let's put it this way. India is the second-biggest country in the world as far as population is concerned. China disappeared behind the Iron Curtain just four or five years ago. This was a complete disaster for the whole free world. If India should follow in the wake of China, as far as I can see, all of Asia would go. The Middle East would be threatened, and the whole balance of power would shift very dangerously towards Moscow. I'd like to add this. The point is not whether India is friendly to us. The question is whether India can succeed as a democracy in building an independent country. A country that's prepared to defend itself against any aggressor, not because it suits our purposes, or not because she's doing it for us, but because she has confidence in her own future and wants to defend what she believes in. Ambassador, how do you see Nehru's historical role in the world? Isn't he indispensable to the democratic world? Well, a lot of Americans become very angry with Mr. Nehru, and he's very complex. They become very frustrated. They don't understand some of the things that he says. He scolds us, he moralizes, and we become very irritated. But I think it goes back to the first question to Mr. Leceras. If Nehru succeeds in building a free India, Nehru will have performed a tremendous service to democracy, because if India can prove that democracy will work, and 360 million people, very poor, all kinds of problems, then freedom and democracy will have had a tremendous shot in the arm all over the world. Well, Mr. Bowles, may I ask you this question? Is this a one-man government? What would happen if Mr. Nehru were to pass from the sea? Well, I wouldn't want to see that happen right now, even though we do get somewhat disturbed sometimes, and some of the things he says. The difficulty in India, I think, is it's basically an old man's government. The Indian Congress Party had worked, of course, for 30 odd years to try to become free from Britain, to throw off British power. The people who run India today, the most part, the top leaders, are in their 60s and 70s. Nehru and them have been to prison together for a great many years. They served in British prisons. There are five possible successes to Nehru that are sometimes talked about and discussed. Their average age is 70 years old. Now, Mr. Nehru is 64. I feel that if he has six or eight or ten more years to try to pull this very complex country together, hold it together and give the people a better life, then I think successes will follow and democracy will be entrenched and very solid. On the other hand, if anything should happen to him tomorrow, I think it would be a disaster. I saw in the Indian press this week a condensation of an article you wrote in Harper's Magazine about communism in India. Communism is well under control there, isn't it? I've been away a long time from the country. At communism in India, first of all, about five percent of the people voted communist at the last election. That was in 1951 or 1952. I don't know whether there are any more communists today or any less communists. There is no way of telling. The difficulty lies, I think, in whether or not, again getting back to this question, can the country succeed? The communists are betting, of course, that it can't succeed. They're trying to tear down democracy in every possible way. They're throwing every obstacle in the way of the programs of economic development and health and all the rest. Now, one of the difficulties, I think, lies in the young people. Young people of India made them are frustrated. They're not trained to become doctors or school teachers or engineers or agricultural experts. They graduate with all kinds of degrees, but they're not really prepared to go out in this big job of building India. So they get frustrated. And the communists come along and offer them all kinds of inducements to join the Communist Party. And some do, not a majority, but some. One thing I learned in Asia is revolutions are not led by hungry people.  They take advantage of the poverty, of course. Mr. Bowles, I think we all agree that India has not been too pro-communist, but don't you think she's been rather lenient towards the Chinese communists and giving us a lot of free advice? Oh, absolutely. You're quite right on that. But India has handled her own Communist Party very sternly. When I got to India in 1951, I was told that India had more communists in prison than any country except the Soviet Union. Of course, all the people in prison in the Soviet Union are communists, or supposedly so. And India's been very tough in putting down Communist revolts. But I think India is rather too hopeful that Communist China may turn out to be something different. She feels that Russia and China eventually will fall in a disagreement and fight, and there'll be a general falling out. She doesn't think that Mao Tse-tung, the Prime Minister of China, will be a Tito. They simply feel that maybe the Chinese will become independent of Russia to a degree. And that's what they're betting on. During your years as an Ambassador, Mr. Bowles, did you feel or were you conscious of any competition between Communist China and Mr. Nehru's India? Well, the competition is in the economic field. You see, Communist China is saying to Asia, democracy can't work.  You've got to follow this ruthless way of communism. And they're urging all of Asia to follow the Chinese way. Now the Indians are trying to build a democracy, and they say that we're determined to keep the freedom of our people. We're also determined to give them more bread, steel mills, a modern country. In that sense, there's a competition between two different methods of progress. It's hard to call communism progress, but what I mean is they're trying to build an industrial state through communism. India is trying to build it through democracy. In that sense, there's a competition between the two. You have traveled widely all over South Asia. Don't you think Nehru's views largely reflect opinion in that region? Well, they affect the opinion of a great many people in the Middle East, Indonesia, Burma, all through many sections of Asia. To a large extent, you hear the same views in Japan. Of course, you'd find many people disagreeing in South Korea and, of course, in Nationalist China, Formosa. There you'd find violent disagreement. There are really three Asias. There's Communist China, 500 million people. There's the Asia of Mao Zedong, of Chiang Kai-shek and Sigmund Rhee. And there's this other neutralist, independent, uncommitted Asia. But that uncommitted Asia, and Nehru does, for a large extent, speak for them. There are about 600 or 700 million people. They may be decisive. Mr. Bowles, an ambassador, is sort of a super reporter. Now, when you were in India, did you come across any great Indian problems that you thought you could give some recommendations to the Indian government about? Well, one thing I felt very strongly about was land reform. You know, in America, we all take the right to own some property for granted.  Well, the Indians are no different. And where you have land owned by thousands and thousands of acres, owned by one man, with the people working the land paying a tremendous rent, raising food out of the hot sun and giving 50, 60% of it to landlord, you have a ready-made area for the communists to move in and take over. I think it's very important that India puts through these reforms so that her people really feel that they're making progress and that they own their own little farms and that they're not just working as serfs for some great landlord. That's one thing I felt very strongly about. Ambassador, what do you think of this new American plan to build up the Pakistan army? Won't it upset stability in Asia? Well, I don't know. I wish that India and Pakistan could get together. I think it's tragic that they're not together. And I think that the whole world, the United States, and all the free world should do everything possible to encourage the two nations to get together. Now, if they were friends, and they should be friends, they're the same people. If they were friends, they could do a great deal to help fill this vacuum of the Middle East. There's a dangerous vacuum there. There's great confusion there. There's a danger the Russians may someday move into that vacuum because aggressors always move into vacuums if they can. There's another dangerous vacuum in Southeast Asia. The whole Thailand-Burma-Indochina situation, which is having its difficulties. Now, if only Pakistan and India could work together to help these neighbors and theirs to bolster themselves up, then they would make a very great contribution. Now, I think any policy that tends to divide India and Pakistan and make them quarrel more heatedly than they have in the past is wrong and mistaken. Mr. Bowles has a final question. May I ask you, is there any advice you could give us Americans on how to better our understanding of the Indians? Well, you might almost say Indian Asia. I think what my advice would be this. That we go back and remember our own early future as a country. When we were very young, we were very independent. We were very sensitive.  We didn't want any foreigners telling us what to do. And when they did try to tell us what to do, we scolded them back in no uncertain terms. We were isolationists. We were neutrals. We were behind these great oceans. We didn't want to have any part of foreign wars. All of this applies to India. She too is trying to build her own country. She's trying to make democracy work. And I think there's a great similarity between our own experience and that of India as a very young nation trying to find her way. Thank you very much, Mr. Bowles. It's a great pleasure to have you here tonight. Good to be here. The opinions you've heard our speakers express tonight have been entirely their own. The editorial board for this edition of the launch in Chronoscope was Larry Lasser and Krishna Baloraman. Our distinguished guest was the Honorable Chester Bowles, former ambassador to India. A priceless attribute of every long gene watch is pride of possession. It brings to his owner the satisfaction of knowing that he owns the watch of highest prestige among the finest watches of the world. Yes, a long gene watch brings its owner more than the delight of a beautiful possession, more than the unsurpassed timekeeping of a remarkable watch. For that long gene watch of yours is the one and only world's most honored watch. Only long gene among the world's finest watches has won ten World's Fair Grand Prizes, twenty-eight gold medals, highest honors from government observatories, and a position of preference in sports, aviation, and in science. For an anniversary, a birthday, for any important gift occasion, no other name on a watch means so much as long gene. Yet, unbelievably, you may buy and own or proudly give a long gene watch for as little as seventy-one fifty. Long gene, the world's most honored watch. The world's most honored gift, premier product of the Long Gene Witner Watch Company, since 1866, maker of watches of the highest character. 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I've known two or three hit men, they're very quiet guys. Yeah. And we're cutting old worship. Yeah. In Louisiana. You just listened to a snippet of a conversation that took place on March 6th in McCurtain County, Oklahoma following a Board of Commissioners meeting and it featured several prominent local officials including the County Sheriff, Kevin Clarty, his investigator and multiple commissioners. Now a local newspaper, the McCurtain Gazette News broke the story and they released a transcript of the conversation but now we're getting the audio.  Not only do they allegedly plot the literal assassination of local journalists but they also talk about their desire to assault and murder black people. It is genuinely deranged and when you get more context after we listen to it you'll understand how sociopathic these individuals are. So we're gonna listen to a portion of the audio now but before we do, just a content warning obviously you're going to hear very violent racist rhetoric that is deeply disturbing but without further ado, here's what they said.   Yeah. Well it's not like that no more. I know.  But you can't do that. The thing about it is... They got more rights than we got. What they really don't know is they... And that's the thing about it, 20 years ago I wasn't doing something like that. About 20 years ago it was in a political position. They're insignificant in my life. Yes. Really. They bring... No saying. It goes around, it goes around. It will. I told you it will. Yeah. I know where two big depots are there every day. I got an escalator. These are our free dogs. Yeah, but the thing of it is, you know... We actually told the truth. I've known two or three hit men. They're very quiet guys. Yeah.  Yeah. And losing them.  Yeah, but here's the reality. If the hair on his wife's head, her son's hand's head, or any of those people that really were behind all that, if the hair on their head got touched by anybody, who would be the bad guys? Who did you plan for? Yeah. On a bark body, you know, we wrap it up in tinfoil to preserve the body and stuff like that. And we put all the body parts and stuff like that. And Kyle was out there, I tell you. All the body parts. Oh, she'd come apart. It's the bark bodies. I know. I picked up one. And... They really fell apart in their bark. And... Yeah.  That's another thing. Yeah. Same. Come on, man. Kyle, I think I got his glasses off. And I got to wrap the tinfoil and put his body back. Kyle goes, you do know what we've got to do now, right? And Kyle goes, no, what? She goes, she's got to preheat the oven 350 degrees, leave her in there for 15 minutes. And she went... Yeah. Now, a lot of what they said doesn't really make sense because we don't have the full context. But when you learn about what they're talking about, it gets so much worse.  That's when she died. That's what they're joking about there. So the Heartland signal explains the transcript suggests that the group first started discussing a recent fire, which killed a woman and her two dogs. The group joked about the woman's body parts falling off her body and that it is similar to eating barbecue. Now, as for the journalists, they were talking about assassinating, quote, Manning chimed in and claimed nobody would care if two of the Gazette News reporters were harmed, quote, yeah, but here's the reality, Manning allegedly said, if a hair on his wife's head, Chris Willingham's head or any of those people that really were behind that, if any hair on their head got touched by anybody, who would be the bad guy? The trio was supposedly frustrated with the Gazette News portraying the sheriff's office unfavorably in their reporting. Now, in the audio, you also allegedly heard Commissioner Mark Jennings referring to two pre-Doug Holes, and that was seemingly in reference to Gazette News reporters, Bruce Willingham and his son, Chris Willingham. Now, as for the racism, I mean, there's really no additional context there.  That's it. Now, Commissioner Jennings, who is the one that claimed he would run for sheriff, if you could still legally violently attack and assault and kill black people, he claims that black people have more rights, and you heard this in the audio, he claims that black people have more rights than white people because you can't legally murder them. I mean, these people are genuinely disturbed mentally and they should be in no position of power. Now, since the audio leaked, the FBI has opened an investigation into them, obviously, since they are literally caught plotting and assassination allegedly. And it's so bad that Oklahoma's governor, Kevin Stitt, has also called on all of these officials who have heard here to resign.  So it gives you a sense of the gravity of the situation if the Republican governor is saying, all right, this is so bad that you all have to resign immediately.  So the McCurran County Sheriff's Office put out this statement on Facebook and we're gonna read the entire thing because it is that good. Quote, the last 72 hours have been amongst the most difficult and disruptive in recent memory. This is a very complex situation and one we regret having to address. There is and has been an ongoing investigation into multiple significant violations of the Oklahoma Security of Communications Act, Title 13, chapters 176.3 and 176.4, which states that it is illegal to secretly record a conversation in which you are not involved and do not have the consent of at least one of the involved parties. They're really burying the lead here to talk about how illegal it is to record them. But anyways, to continue, there is a significant number of victims of this criminal activity and it has taken significant effort and time to identify them and corroborate evidence. Many of these recordings, like the one published by media outlets on Friday, have yet to be duly authenticated or validated. Our preliminary information indicates that the media released audio recording has in fact been altered. Wonder if they're gonna say how? Spoiler alert, they're not going to. The motivation for doing so remains unclear at this point. Really? Really, the motivation is unclear? That matter is actively being investigated. In addition to being illegally obtained, the audio does not match the transcription of that audio and is not precisely consistent with what has been put into print. Multiple agencies are assisting in this ongoing investigation. As a result of the press release that went out on Friday, a large number of threats of violence, including death threats, have been made against county employees and officials, their families and friends. There will be continued press releases from this agency as the investigation comes to a close and findings are forwarded to the appropriate authorities for felony charges to be filed on those involved. Okay, my initial response to that is, phew, just absolutely mind blowing. Their response is basically, how dare you illegally record us plotting the assassination of journalists and black people? How, how do people like this exist? Like the audacity of these motherfuckers to put out that statement after they were caught in 4K, plotting the assassination of journalists. And it seems like they were seemingly, I guess, trying to cultivate sympathy because they received death threats in response to them openly discussing the murdering and disposal of the bodies of journalists that they didn't like. Look, you just, I don't even know what to say.   Oh, and also the audio was altered according to them. Just admit that you were caught red-handed and resigned, but they won't. At least at the time that I record this video, they have not stepped down. They are remaining defined. So I really don't know what else to say about this story. I don't think that my commentary will do it justice here. Just when I thought that elected conservatives couldn't possibly surprise me, this audio gets leaked. And then my mind is blown once again.  Because holy shit, just when you think that elected officials have really sunk in as low as they possibly could have, they go and do this.


OK, we're going to get started. My name is Jules Damjee. First, one is Diaz. Good morning. Thank you very much for coming to my talk.  So some of these are going to be a bit repetitive, but I believe the repetition is good for you to agree. It's part of reinforcement learning. So some of the slides might be repetition, and then we'll actually get into the more needed details of how to use the MLflow API. So this is what our schedule is going to be like. I'm going to talk about the challenges of some of the machine learning problems that most of you actually are working with. What are some of the challenges? How is it different from, say, doing software development engineering in the normal sense of the word? And then I'm going to talk about how MLflow tries to address this. Note that MLflow is the first one, but it tries to address it in a very unique way. And then I'm going to talk about in detail what are some of the components that comprise of MLflow. And then finally, we'll actually get into a demo where I talk about how do you actually use the neural networks using Keras and Python to create a baseline model, which is very normally the case when you're actually comparing models and do experiments, and then create a statistical power whereby we know that a baseline model gives you enough accuracy, but we want to somehow tune parameters and how do you actually do that using MLflow API to experiment, and then compare some of the models as we go along. And then I'll end with a bit of a road map. Now, how many of you actually are Python programmers here in this room? Brilliant. Anybody use Keras? Intensive flow. Lovely. So what we're going to be doing is building three models using Keras and then looking at how we can actually tune the parameters. But the important part is not as much as learning how to do neural networks, but how MLflow can actually help you in tracking the experiments as we move forward. Now, yesterday here about the machine learning is actually complex, right? It is not something that is very similar or very same as your normal software development cycle. There are a lot of different steps involved. There are a lot of different stages involved. And it's a very iterative process, which means that you have to repeat certain things. And it's not like you're just using one set of tools. You're using myriad set of tools. You're using different kinds of parameters. You're using things at scale. And it's imperative that you have the ability to reproduce certain experiments. It's very imperative for you to be able to actually somehow export their particular model. So that in itself introduces a number of complexities which somehow make this task very complex. Now, this is a quintessential cycle of repetitive things that you actually do in machine learning. You've got the data preparation part. You get the training. You deploy. And then eventually you move to the raw data and monitoring of the particular model. Now, each stage has its unique challenges. Each stage has its unique challenges. Well, those are. But for one, in data preparation, you will have loads of tools, unlike software development. You might just have one database. You might have just one streaming engine from which you're actually extracting data and you're doing some feature engineering. But over here, because of the fact that we have today multiple sources of data coming from different directions, you won't be able to actually use the most of the state of the art data sources. There could be SQL database. There could be Kafka. There could be a streaming engine. There could be Flink. There could be Python, Pandas that you want to use. And so each of these actually have a different configuration that you got to worry about. So that's stage number one. You want to use best of the tools. And you want to be able to actually say, I want to use a particular algorithm of choice that is conducive to the problem I'm trying to solve. So that's challenge number one. The second challenge is that each of these have different requirements in terms of how you actually tune. And especially those of you who are actually familiar with machine learning algorithms, you have the ability to actually do hyperparameter tuning. You have the ability to actually change some of the parameters that actually affect the metrics. And as you explore what are some of the tuning parameters, that actually can move the needle in terms of getting two or three percent accuracy on your validation loss or drop of that on your validation loss. Accuracy actually makes a huge difference in your revenue and also on the confidence of your prediction model as you put it in the production. So number two challenge is that you got to have tuning parameters. And then how do you actually track all these parameters? You might use five different parameters that you want to use. And eventually, if you're happy with the eventual outcome of that particular result, you won't be able to reproduce that. You'll be able to track that somehow. And so that's challenge number two. There are different kinds of parameters and you got to worry about that. The third thing is scale. We are entering the Zeiss geist of big data. And in other words, big data now is actually a nomenclature that we see every day. And things happen at scale. And I would contend that the more data you actually have, whether you're using deep learning frameworks, whether you're using deep learning models, or whether you're using traditional machine learning models, the more data you have, the chances are that you have a better accuracy because your training model can actually look at data that actually hasn't seen. And if you have a large validation set, then you can actually have a confidence that whatever I've trained on something, I can actually get a fairly good high accuracy on the unseen data. So having a large set of data at scale is important. And scale is important not only on one stage of the machine learning cycle. Scale is actually important throughout because after you have done the training, you're going to do a deploy. And then when you're done deploy, you actually put it out into real production. And you're going to be monitoring data how your model is actually working.  And so this particular cycle actually repeats itself. The challenge number two is that you've got to worry about scale. And that's an important part of it. The third thing is once you're done as a machine learning developer, you're going from feature engineering to training. And now you're going to hand off the model exchange to your ops guy and how he or she is going to take that particular model and ensure that the model that you actually hand over is the one that you actually experimented, the one you actually tested and the one you were happy with so that he or she can actually deploy it. And you can actually monitor and then repart that. So model exchange is an important part in this entire predictive cycle. And then finally, we are living in an era that GDPR is now an imperative part, especially in the EU and also in the United States, where your private data is actually something very personal and a lot of companies have had problem either selling their data or inadvertently exposing your personal data. And so it's very important for you when you deploy a particular model, you have an accountability, you have a governance, you have a provenance, you want to make sure that when you deploy the particular model, you have the ability at any point in any time to be able to query that. Who ran the particular model when it was drawn, what data was used, and so on and so forth. So collectively, these are kind of things that daily people who are doing development in their life cycle encounter that. And people do their own un-court and overflow internal tools whereby track that, they might do it on a spreadsheet, they might do it in a JSON file. And then they have to somehow create a report to sign over. These are the tuning parameters used. These are my configuration. This is where the data came from. This is the code version that I actually used to create that model. All that bookkeeping can actually be taken care of a tool such as that allows you to keep tracking. And when we were doing research, when we were talking now to developers, and we were talking about the customers to find out what is it and how they're actually doing it. And it was not very dissimilar to what we actually would automate that. And this was one of the code that actually came from one of the very chief data scientists who say, we develop models on a daily basis, 100 to 200 models. And we have data scientists who are our programmers. We have data scientists who are Java programmers. We have data scientists who have statisticians and ML developers who swear by Python that don't use anything else. We want to be able to actually create tools so that we can actually deploy them. And it becomes like a religious war where which one should we actually use.  Now you might ask at this particular point, Jules, what's the problem, right? I mean, if this is such a problem, why people actually haven't sold it? And it turns out that people have. There are large companies out there who actually do this on a very frequent daily basis. They go out developing tools. And so we did some research to find out how these big companies, Facebook has got enormous amount of data. They have machine learning models that they deploy on a daily basis. It's not only one model, they have several models, and they do a lot of A-B testing. And how do you actually track all the different requirements that actually, how do you actually track all the parameters and do that? So they have their own internal tool called FB Learner, which is fine because it standardizes the way they train them more than they deploy it and how they actually monitor it, which is good because now we can actually learn something from it. Uber is another one that came to the Michelangelo, which is very similar in terms of that allows them to be able to do all that. And so is Google. I mean, when you talk about Google, what do you think about? You think about scale, right? These guys are dealing with scale amount of data and with the TFX extended server that can actually deploy various kinds of TensorFlow model, not only in one architecture, but also in your mobile phone, also on Raspberry Pi, surprisingly. And all these are requirements that do this. The only problem over there is that while it's true that they give you the ability to actually do this, but they're limited in terms of the choice of algorithms you need, the choice of frameworks that you need, the choice of programming languages because they will adopt the one that's commonly used within their company which actually suits their network and they're tied to the company's infrastructure. So when you leave the company, what happens? But you lose that intellectual property. And I remember one particular anecdote when I was running a meet-up in San Francisco and I had a guy from Facebook who was a data scientist who actually gave me a talk in how he was actually doing A.B. testing and calculating the sentiment analysis on the comments, right? When you look at a Facebook comment, you say like or you might comment and they want to find out how do you actually do sentiment analysis on this? And he actually gave a very eloquent presentation on how he actually does that. And at the end I asked him, you know, what was the underlying library you actually used? Did you use Spark ML Lib? Do you use this particular algorithm? And he was dumbfounded. He couldn't answer the question because you know, we actually have a very abstraction layer. All I do is I create all this high-level declarators in my file. I said I want to use a logistic regression. I want to use this particular function. I want to use, here's my data. Go ahead and create a model for me and then run the model and give me the results. So that's a high-level abstraction. Now if he left somewhere and he won't be able to do that. Same thing with Google's Borg, which is a massive scale in how you actually do provisioning. And when people left Google and went to Twitter they couldn't provision things because the Borg didn't exist. It was a very platform that was very specific. So we decided what is it we can actually learn from this that standardize things but we want to do it in a very open manner so that when you leave the particular company you don't have to worry about taking the intellectual property. We can do that in an open manner. And one of the ideas behind OpenMan actually came from the Apache Spark community because the founders who created MLflow are also the founders who actually created Apache Spark. And they learned a lot from the fact that doing things in an open manner is where the innovation happens because if you look at historically how open sources actually change the world innovation happens in collaboration. It doesn't happen in isolation. And that was the principle behind what we decided to do was to keep this in an open manner so you can actually do that very easily. And thus came MLflow. And MLflow was created with a couple of things in mind. One was that we wanted to have the ability to actually run any algorithm that you actually need, any particular framework you want to use, any language if you don't have language wars. We wanted to be able to run in any particular environment locally because a lot of people developers would run things on PyCharm or they would use ID. At the same time run it on the cluster as well or on a large scale. We wanted to make it very simple. We want to make it easy to use. And I think developers are where the power is. They are the new king makers. And you provide them simple APIs. You provide them modularity so they can build things on their own. You really have the minds and hearts. And we always wanted to keep developers first. And thus was the design philosophy. And the design philosophy was very simple, API first. If you look at history, how APIs have actually changed in how you actually build something, you look at Unix for example, see API changed completely how people started writing stuff. You could write shared libraries. You could write window managers. You all ran on the same platform. Why? Because there was a C interface to the kernel. There was a C interface to the application layer. There was a C interface. Very well defined signatures in C. Then you can actually write those applications. Take an example Java. Java came in late 90s and it completely exploded the world. Why? Because of the APIs. Developers love the APIs. So we wanted to stick with the ideas that you give them the APIs, make them simple, make them modular. So that was the first design philosophy. That was the principle and the requirement we actually did there. And this sort of allows people to be able to actually build these things using the APIs and REST APIs as well. The second part was that we wanted to actually make it modular in that we didn't want this particular tool to be monolithic, right? We wanted to actually have modular tools so they can use it distinctively or independently or they can actually use it collaborative to create an entire MLflow structure. So there was a whole idea behind that. Keep it distinct, keep it less monolithic and keep it simple. And the whole idea is if you have something this decouple you can actually build things like that. That's how Unix was built. You got shell, you got all these different commands that you can actually do. You can stick together and now you have a full bone application. And so that sort of was the principle and the proponent proposition behind MLflow. And that's how we actually went forward and did that. And if you look at the MLflow components there's actually three very distinct components that can be used independently or that can be used together, right? So the MLflow tracking allows you to track your APIs, allows you to track all the hyper parameter tuning, allows you to track and persist all the parameters that you use, all the models that you actually use. And so that is a very simple API. You take the API, you track it. And the second thing was project. And project is a way for us to be able to encapsulate your entire existing project as your model. And that project can become a unit of execution in any particular environment because we want it to sort of encapsulate. Think of project as a docker file, right? Docker file has all the dependencies that you actually need. And once you actually deploy the docker in the container manner, you can run pretty much anything. So that was the idea behind docker that the project is a very simple way to express those things. And that way we can actually encapsulate all these different models in this particular project. And then finally, the third one is model. And think about model as a way just like project where you can express the model in one particular flavor. And the flavor could be a TensorFlow model. The flavor could be a Keras model. The flavor could be an MLip model. And for us to be able to actually requirement to have all these ML models, if we supported all of them in their own different particular format, we would actually have an N by N matrix multiplication. So if you actually somehow use that idea by saying, well, why don't we come up with a convention where we can actually express the model in particular flavor? And then that flavor can actually run in any environment where that flavor is actually supported. So let's look at tracking. There are a few concepts in tracking that people worry about. And those are normally parameters. Those are things that you want to track. Those are key value periods you can actually use. Numeric values are matrix that you actually want to capture. So they might be, say, for example, a root min square error that you actually got that you want to track. You might want to keep track of the binary loss. You might want to take out the validation loss. So those are the metrics that you care about. Those are the things that actually move your needle. Those are the things that make a difference in your model. There might be tags and notes. Those are sort of, you know, oh, here's a note for a reminder for me to actually remember something. And those are, the artifacts are things that the model uses. This could be files. This could be a code version. This could be data from where you actually did the preparation. This could be any number. This could be a source code. This could be a revision that you actually used. It could be the model you actually saved. Those are the artifacts. And the sources is, you know, what was the source? What was the origin in where it actually did? And then version is obviously version of the code. So those are the essential things that people want to be able to track it so that when the model is created with this particular experiment, I can always go back and exactly find out where the particular model is. And the tracking API is very simple, right?  And then using the compound statement such as with the current run, go ahead and log this parameter, log that parameter, log this particular metric and so on and so forth. And so very simple API that allows you to do things. And finally, tracking has this notion that I can have a tracking server running locally on my machine, or I can have a tracking server running somewhere remotely on the cluster where I can actually do the tracking.  And then finally, setting the tracking to run on a particular remote server which could be production or which could be dev. And those can be run in three different ways, right?  If you have MLflow installed as part of the package, then you can just import those and from your notebooks go ahead and do the tracking. Or you can actually have local application on PyCharm that actually just says tracking server is here, go ahead and run it, or tracking server is not set. So I'm just gonna do everything locally and once I'm happy with it, I can actually do the experiment running remotely. Or it can actually be part of the cloud jobs whereby you might have a cron job that's actually running this model on a daily basis to do the training and then tracking all the experiments remotely. And that's sort of governed by these two attributes. You can actually, on the command line, you can actually say, here's my environment variable. Go ahead and run this experiment and put all the data on the tracking server. And if this environment variable is run and I'm running locally, it's gonna create an ML run directly locally and you'll have all the experiments there. And then, or you can actually set it in your model where you're actually doing the experimenting where you set the tracking to be somewhere else and now you're actually going to do it. So that's sort of a tracking. We wanted to be able to take this motivation and take it one forward and say, well, the motivation behind creating projects as an independent way and as a way to express that was the ability to create this project file called a project format. And this project format encapsulate what your project is, what your model is, what flavor it is. And that way, this allows us to be able to run this flavor on any particular machine. And it makes the productionizing a lot easier and it makes it easier to manage. You don't have to create thousands of different project files or different kinds of projects because one is Keras and one is Sidekid and one is the other one. You have one project that expresses what your project is and it becomes a unit of execution. And so the idea behind projects is very simple. I can actually run it locally or I can run it remotely and think of project as a Docker file, right? I express my execution or mode of execution in my project file and I deploy that in a container and it downloads all the necessary dependencies that I have and it can actually run. So that's the whole idea behind every project. And it encapsulates three very important things that comprises your model, the code that you actually use to do that, the configurations that you actually use that and the data, the files where you actually got the data from. It could be a Kafka server, it could be a SQL database, it could be HDFS path where you actually have large amounts of data where you actually use to train your particular model. And those of you who are coming from the Knicks background, the file project is very simple, right? It could be part of your Git repository where the name of the ML project is, you know, Keras IMD classifier. And then the project file is just nothing but the YAML file that says, here are my entry points. When you run this particular project and if I give you the parameters, use this default parameters or use this Python code to execute that.   And you actually have a self-sustained project running where you can actually do that similarly. So it's a very sort of very easy intuitive way to express what your project is. And you can run project in number of ways, right? You can say on the command line, I can say ML for a run. And if somebody has published this project on a Git repository, if I just go to the Git repository, it will download on my local environment. It will use, it will ascertain from the Kanda YAML that I need these packages and I need SciPy and I need Pandas and I need TensorFlow and it carries. It will download those and it will run in that particular Kanda environment or a virtual Python environment and do the tracking experiment depending on where the tracking server is. So it's very flexible, it's very powerful, it's very simple conceptually to actually understand that. So that sort of is the idea behind project. And then the ML models, now this was something that initially we had a bit of difficulty how to articulate, but the way I look at it is MLflow models allows you just like the Docker file and I'm using this analogy over and over again is because models just like projects are a way to express how that particular flavor is. Now the models could be a flavor or a carousel, it could be any of the deep learning frameworks or machine learning libraries that you perform. And once we encapsulate that particular flavor, you can think of this flavor as a Lambda function. Those of you are Python programmers understand what Lambda function is. You actually take the Lambda function, you deploy it anywhere that's running Python and you just have one function called predict and you can actually use that particular predict function or you can actually have the flavors at TensorFlow that's running on that particular Docker environment. And if TensorFlow is there, it will know that I'm running a TensorFlow code. I'm gonna load that particular model using the TensorFlow HD format or carous and I'm gonna execute my predict function given the particular output. So that sort of allows us to do that. How many of you use Apache Arrow? Anybody use Apache Arrow? So Apache Arrow is very similar in terms of deserializing and serializing Panda's frame to allow applications to run on JVM. So instead of actually JVM trying to understand I'm gonna have Mlib, I'm gonna have Mlib function, I'm gonna have Arrow function, I'm gonna have all these different formats to understand. Arrow is an exchange format. I'm just gonna have one format and the JVM will know that you're actually using the Arrow format to serialize and deserialize. So in a very ways, it's very similar where you can actually exchange the model. And if you look at the model file, it's quite simple. You have the model that says when the model was actually created, time it was created, what flavor it is. So I can actually use TensorFlow where I'm deploying this particular model to say, well, this is a TensorFlow model so I can load using the TensorFlow API to load that particular model it was saved and I can use whatever the model API on the TensorFlow is to predict, to evaluate, to do whatever I actually wanted a particular model. And the second is a very generic model called Python Flavor that is very generic. It can run anywhere where Python is running as a Py function, as a Lambda function. So those are sort of the two flavors that allows you to deploy this model anywhere where either you have the ability to run Python or you have the model target on an environment with TensorFlow, Keras, Spark, MNLip, and so on and so forth. So I think this is a time where you get to see some code because I know yesterday there were a lot of high level stage so I thought today I would spend the next 15 minutes I have on code. Now it's going to be quite challenging because it's going to be hard for me to type but I'll try. So what's my model? You might want to take a picture of this because you can actually go home and look at the GitHub if you missed some parts of it that actually has a notebook that allows you to do that and there's a blog associated with that and all the code that I'm going to show you today is there so I don't have to endlessly trip all the wires while I'm talking and have this cell tape here. All right, so this is what we're going to do. We're going to create three Keras models. We'll create the baseline model that says, okay, this is my baseline model and once I have the baseline model I look at the accuracy to see whether it actually makes sense and I'm going to use now two experiments, experiment one, experiment two while I'm changing the hyperparameter tunes to see how is the needle moving? Do I have a better accuracy? Do I have a lower loss? Is my model overfitting? Things of that sort. And these are the things you ask when you're actually experimenting and you want to capture all the parameters available. So we'll have a neural network that actually looks like this. We have an input layer that's going to take the IMD classifier as a vector of input from the Keras vectorized as tensors and we'll have a number of hidden layers. We can experiment with the hidden layers and the last layer is going to be an output layer which is sigmoid as your function and those of you are familiar with active function, sigmoid, it gives you the ability to actually give you a probability or watch your classification is between zero and one. And so anything that's above 0.5 will say it's good. Anything below, it's going to be a good API. I'm in a good prediction. All right, so let's look at that. So what I'm going to do now is if the demo goes up with me, could we switch to my screen? I can't seem to see my browser. Do I have to do anything different over here because I can't seem to see my browser? Okay, so the demo goes up with me.  And what I'm doing, I'm going to actually create three particular models. One is the one which is a base experiment. Oh, actually you can see it. Yeah, but I'm trying to go to the system preferences. I actually want that, I want this particular screen, you know, the browser, so I can type things in. Okay, looks like the demo goes up with me today and I can't seem to find my stuff. But the whole idea is, and I tried this before. Yeah, I'm not getting, does anybody know how to actually change their preferences so I can actually show the demo, I can't seem to get the browser up. I want to be able to actually see the screen over here for that particular browser to the Chrome. That's where I actually had the experiment. So I can actually show, no, the browser is here, the Chrome. But I can't see it over here, right? So we just, yeah, yeah. So if you actually go to preference in a mirror, I should be able to see that over here. Yeah, bring it over here, brilliant. All right, so in the interest of time, I actually have this experiment that we're actually running already. And what I've done essentially is I have, I've got nine minutes, I'm gonna end it right quickly. Can you guys see this? You can't see it. Oh dear, okay. All right, this is unfortunate. I should be able to see it. I can see it on my screen, but I can't seem to get to show the mirror. Okay, so just to give you an idea, I mean, if you look at this particular code here, I can't even look at that particular thing. All right, what I'm gonna do is, probably take any questions you have. I think the important part was to actually show you a demo, how to actually use that. I'm gonna be at the Databricks booth, or I'm gonna hang around here after that, and I can actually show you personally. But the whole idea is we actually create the experiments. We launch the MLflow UI to actually look at the experiments and see how we actually lowered the metrics and how we actually do the loss function. So I have a few questions that I have some time. I can answer any question they actually have. Unfortunately, the demo codes are not with me. I can't seem to mirror the preferences. Otherwise, this would have been an interesting exercise, but I can only show you later on if you stop by the booth. Any questions you have? Yes. Hi, nice talk. Thank you. So our stack is based on Python, ScylerKit, we deploy it with Docker. And our format is JobLib. Is this MLflow compatible with that? Yeah, so if you're using Cykit-Lan as one of the frameworks, one of the flavors that we actually support, you can actually save the model as an MLib model, and they can deploy that as an MLib model, and it will save both as an MLib or as a pickle in Cykit-Lan, whichever way you actually want. So if you're using Cykit-Lan, you have two choices. You can save it as an MLib model, which will use the MLib format to store it, or you can actually use the Cykit pickle that actually used that. And depending on where you are storing on the Docker, you can load it using Cykit API to load the particular model. It will load the pickle file if you saved it as Cykit file, or if you just leave it as MLib, then you can just use the MLib model, load it, and then just provide the data frame as a pandas data frame to your predict in the model and you get all your results. I wish I actually had the demo. They've been actually worth it, but I apologize for that. All right, any other question do you have? Yes. Thanks for your talk. Thank you. Are there any plans for the MLflow project to run on Microsoft platforms? Well, actually, yes. We have the ability to run MLflow on Azure Databricks, that actually runs on Azure Databricks. It runs on, you can deploy your models on Azure ML, right? And we can also deploy models on AWS SageMaker. So yes, it actually runs on Microsoft Azure ML. So if you, with the current version, we provide you the docker file so you can actually run on Azure ML. So any model that you actually save, it can be deployed in Azure ML as well. And as the CNTK from Microsoft, like TensorFlow works, do you have any flavor for that? So CNTK is on the list right now. We support the most popular ones, but if you feel that there is a need for CNTK, again, this is an open-source project. I think it's people like you, really, who actually make a difference. And we want to be able to actually do that, whereby you just file in a pull request, and we're very open. Some of the things that actually have come out, for example, the RAPI, the scatterplot, how you actually use it, has actually come from open-source community. And so we're very open. We're still in alpha 0.8, and we are very, very conducive to people actually putting a PR request. And we evaluate the request on daily basis, and we create releases on sprint, on bi-weekly basis.  And I'll be there to actually take it and take it to the right people to say, okay, we need CNTK because we have people actually using it, right? Because we actually deploy your Microsoft Azure ML, it's not unreasonable to think that CNTK right now. But because TensorFlow and Keras is now very much integrated in sort of a default platform, a lot of people use that. But I don't see any, I wouldn't say, there's nothing that actually stops us from you finding a PR request and re-approving it and making that part of that. The more mortals we have, the more open it becomes, and people who come from a diverse set of skills can actually use that. Any other question you have? Yeah, we have a question in the back up there. So if you just go to mlflow.org, we've got documentation over there and we have had the GitHub repository and you'll see all the code examples.  Pip install, mlflow, it's very easy. Import, mlflow, start hacking on your local machine. You'll love it, literally. It's, you know. Thank you for your talk. We just, here. Oh, okay, sorry. I just, to ask if you have any plan to include a service, not as an installation, the part of mlflow registry and all of these parts. Good question, good question. I think what you're actually asking is, how do you actually deal with metadata and how do you actually deal with governance? And if you look at the cycle that I actually provided, you know, the governance is actually last part. And on the roadmap that I was gonna show right now because we're out of time, one of the things we actually wanna do now is the last stage, which is the provenance and the governance because I think without that, it's like pasta without garlic, you got to have that, right, as part of it. And I think we are working diligently to cut the releases where we actually now have the entire accountability by saying, I wanted actually not only register who did the experiment, who ran the experiment, when they ran the experiment, were they allowed to run the experiment? You know, can I actually go to the registry to find out what the access control, how? So all those are back, which are part of the enterprise requirements are going to be part of it. Remember, we just really 0.80 is still in its preliminary stages. And I think it's important right now to get the community galvanized and start contributing that. And I believe as an advocate and I believe the company that I work for were the creators of Part Your Spark that lightning can actually strike twice because what happened with the Part Your Spark was not only the community actually contributed quite a bit and it really, really has taken off. See what's happened with TensorFlow. You have Google who actually released it and the community is actually behind it. And we feel that we can actually do the same thing with MLflow, whereby we actually create a lot of PR requests from you guys. And I think you can actually contribute that and you can actually take it and help us to go to the next level. Any other questions? Yeah, in the back. Yes, so last year in our team, we started to work with MLip with the idea of serialized models from Psykit-Learn or from Spark. Don't worry too much about which technology we're using. But soon we realized that it was not so easy because in MLip, it's not supported some of the transformers that we have in Spark. MLflow help us with this kind of issues? I think that's a good question. The question is, MLip has certain restrictions. What is it that MLflow can actually do? Because as you notice that MLip is one of the flavors that we actually, in Psykit-Learn, you can store as MLip. And they are customized additions that you can actually put in your model.  And so, yes, we have plans to document that and provide code snippets to see how you can actually write your custom transform in the MLip model when you actually save that. So when you run, your entry point not becomes the predict, but becomes the transformation that you actually wanted to. Anybody else? Well, if not, thank you for coming. I'm sorry about the demo, but I'm going to be at my Databricks booth, come by and I can actually give you a personal demo. Thank you.


12 websites to make online cash for free. No credit card required.  Then this video is for you. I'm gonna go through 12 different websites where you can make money for free. Now, any of these websites are gonna require some effort. Obviously, you've gotta work for your money, so what I'm gonna do is I'm gonna rank them from the most amount of time you'd have to invest to the least amount of time you would have to invest. If you haven't subscribed so far, please do so, hit that subscribe button down there, turn it from red to gray, and don't forget to ring the bell. Turn on all bell notifications so you're notified each and every time I go live, or if I upload a new video. Okay, guys, you wanna make some side money nowadays? It's easy to do, it's not hard. Anybody can do this. It even doesn't matter what your talent level is, because you can learn a lot of these things, but let's jump right into it and look at some of these sites. Now, the first one, number one, is Fiverr. Fiverr's been around for a long, long time, and it's a great site to make some money, but you gotta use it properly, okay? You wanna put a good profile in there. It needs to detail exactly what you're gonna charge. Fiverr has it set up where you could have a simple level and then a more advanced level of service for your clients so that you can make more money. Now, I will tell you, using Fiverr is like anything else. You've gotta be on top of it. When somebody contacts you, you've gotta contact them immediately. You gotta get right back with them. I know when I post jobs on Fiverr, and I do it all the time, the first people that respond to me are usually the people I hire because I'm like, they're on top of it. Plus, I'm in a hurry. If I'm using Fiverr, I put something there and I wanna get it done right away, so it's really important that you reply to people instantly if you can. Now, let's talk about a couple of the drawbacks. Now, you can make thousands of dollars a month on Fiverr, but it started off as a website where people would do anything for five dollars. So, a lot of people that go to that site still have that mindset, and they wanna, I don't know, get things on the cheap, and I would avoid those people. I really would. I would have higher rates on Fiverr because your time is worth it. Now, as far as the time investment goes, it's gonna take time to set up your account there. It's gonna take time for people to see you. It's gonna take time for you to develop all this. It could be a couple weeks before you see any money, but in the long run, Fiverr's a great site if you want a side gig or a full-time job. The next one is Upwork. That's number two. I love Upwork. I use Upwork all the time. I've hired a lot of people from Upwork. I have people working for me right now that I hired from Upwork. Basically, the way it works is you're gonna pay a commission on Upwork to actually hire people there. It wasn't like that in the beginning, but they have changed and they charge a commission now. But you only pay when you use somebody so that's a good advantage. And it has a wide variety of people that can do all kinds of things from websites to SEO. Anything that you need done in your business, you could probably find somebody on Upwork to do it for you. Now, I guess some of the drawbacks from Upwork is that it's really competitive. There's a lot of people on Upwork trying to get jobs in a lot of foreign countries that are gonna charge a whole lot less than they would if you're in the US or anywhere in the Americas or the UK. However, it's a great site and a lot of people wanna deal with people that are in America or Canada or Australia. So what I would suggest you do is you set up your profile there and look at some jobs and underbid the jobs. And what I mean by that is bid them less than what people are asking for so that you can get a portfolio of reviews. The more reviews you have, the better off you are on Upwork and a good jumpstart for that is just do some jobs on the cheap and let the people know you're doing it cheaper because you'd like to get a good review if you do a good job. Now, Upwork is similar to Fiverr and as much as you gotta put a lot of work in upfront to set up your profile. It can take as much as 10 days just to get approved on Upwork. There is a time delay. They're not gonna pay you for probably at least 10 days after you've submitted your work so you have to keep that in mind also. The next one I wanna tell you about is Etsy. Etsy is a great site. Etsy is especially a great site if you like to make things and sell them. Now, that doesn't mean you have to be an artist or a creative person because if you just wanted to do drop ship, print on demand, t-shirts and cups and mugs, you can also do that on Etsy. But it has to be a created item to be sold on Etsy so that's one of the drawbacks. If it's an item that you can buy anywhere and anybody can buy it, it won't be allowed on Etsy. Now, the good part about Etsy is it's very easy to navigate and very easy to set up a store there so you won't have to put a lot of effort into that. Now, I guess one of the drawbacks would be there's a lot of people selling on Etsy but you could overcome that by having more unique products, more unique t-shirts or hats or mugs or whatever. Have your own sayings, have your own designs or if you're creating products and they're one of a kind products you'll do really well on there. Now, as far as the time investment you've got to consider if you're making handmade items you've got to factor in that time. It takes time to make money on Etsy so depending on how much effort you're putting in there it could take you weeks or maybe just a couple days. But keep in mind when you're new to Etsy they hold your money in the beginning. You won't get paid for about three days after you make a sale on Etsy until you've been there for three months and then you can actually get paid the next day. Number four is task rabbit. Now, we're gonna move on to something that's more physical. This isn't online stuff. People post jobs on task rabbit to what? To get tasks done. This could be something as easy as setting up furniture or putting carpet down or doing yard work. Whatever it is they put tasks on there and try to hire somebody. So this is a physical site, okay? You're gonna use the site to find the jobs but you're gonna probably have to go and do something physical for it. But there's a lot of tasks on there and if you're good at it in your neighborhood you could build up a good clientele and heck you could have a full-time job. Now the drawbacks to task rabbit is you've gotta do physical things. You've actually gotta physically go out and do something but if that's your jam you should do it. Now as far as time investment that's really up to you as soon as you set up your account there you could start taking jobs but keep in mind you're not gonna get paid until the job is complete and probably a day or so after that. It could take up to three to five days to actually hit your bank account but if that's your thing and you wanna get some physical jobs task rabbit's the place to do it. The next one up, number five is Merch by Amazon. Now Merch by Amazon's a great site because all you have to do is come up with an idea, some type of creative and they will actually create the product for you such as a T-shirt with an imprint on it and they'll actually ship it out to the customer. It's a great place because there's so many people going there.  Merch by Amazon is a great place to sell your products. Now the drawback is you're not gonna get rich if you sell a shirt for about $15.99 you're gonna get about $2.21. Not a lot of money however you could sell a lot of items there and not have to put forth a lot of effort to set it up. Now as far as time investment it's probably gonna take a few hours to set it up and if you're a really good artist or you have people who can make designs for you you can get a lot of designs up there real fast. It's a great place, there's a lot of traffic Merch by Amazon, check it out. The next one I wanna tell you about is Neighbor. Neighbor's a pretty cool site. If you've got some extra space where people can store some stuff Neighbor's the site for you. All you have to do is list your space and what you charge and you'll get paid through Neighbor. Now Neighbor does charge a fee of 4.9% of whatever you receive and you get paid on a monthly basis. Now as far as drawbacks, you gotta tell people if you can't rent the space anymore you've gotta give them 30 day notice and if you do an early cancellation they're gonna charge you a $60 fee for canceling early. Now as far as time investment probably a couple hours to clean out your space if it's already clean you don't even have to do that. All you've gotta do is list it. The next one, number seven is Offer Up. Now if you don't have the time and energy to set up a garage sale and sell all your items Offer Up's a great place to do it. All you have to do is snap a picture of your item and put it on Offer Up and people can buy it. You can select a place to meet them to give them the item, preferably a public place where nobody is gonna be in danger. However, they do have an area in there where you can actually ship stuff out if you'd like to. Now Offer Up has been compared to Craigslist many times. However, I think Offer Up's a whole lot easier because they have an app you can download the app and you can send private messages back and forth to buyers. Now as far as drawbacks, you're meeting a stranger so you gotta keep that in mind and you're responsible for your own safety so make sure you, like I said, meet in a public place that you feel safe. Now as far as time investment, it really doesn't take that much time.  Number eight is Wander. Now on Wander you actually get paid for your research skills, okay?  That doesn't mean that everybody gets approved on Wander. You have to fill out an application which is gonna take you about five minutes and once you're approved, you can actually get access to the dashboard and start answering questions. Now Wander doesn't pay you a ton of money. On average, you're probably gonna get $8 to $15 for each detailed answer and that's gonna equate to maybe $20 an hour but it doesn't take a lot of effort either. It's just you providing information and maybe answering questions from the couch. Now I guess the drawbacks to the site is that you don't make a ton of money but you could be making some money in your spare time while you're watching TV or while you're just sitting on the couch or lounging on a beach somewhere. That's not a bad deal. So check it out, it's a great place to make some money. You're gonna get paid about every two weeks and that's through a PayPal account so you do have to have one of those. My next site is number nine and it's Thread Up. Thread Up's a really cool site. If you're just trying to get rid of some items around the house, it's a great place to do it especially clothing and handbags, that type of thing. What you do is you're gonna receive a prepaid label to put on a box to send in your items. Now you wanna make sure you got some good quality items to send in because if they're rejected you can be charged a fee. Generally on Thread Up people are looking for brand name items and Thread Up actually decides what they're gonna be worth and what you're gonna sell them for. So keep that in mind, you don't get a chance to decide what you're gonna sell these items for. Thread Up does that for you but if you're looking to clean out the closet and get rid of some brand name items, it's the way to go. If your items have seen better times and they're not that good a shape you do better off to actually have a garage sale because they can get rejected from Thread Up. Now I guess the drawback would be you're not gonna get paid until Thread Up accepts your clothes but if you're sending good quality things in there you shouldn't have a problem. As far as time investment, absolutely very little time.  The next one, number 10 is Swap. Swap is very similar to Thread Up but they work it a little bit differently.  Now an item that sells for $8 or less you're gonna get 15% plus a 20% store credit. Now for an item that sells over $8 you're gonna get 70% of the price less $4.95, yes $4.95 for a processing fee. But keep in mind what you wanna do is sell higher price items that have a higher value so you can help cover that $4.95. In addition to that they will also give you 20% store credit. Now as far as drawbacks go your clothes could be rejected. If they're rejected just like Thread Up to get them sent back to you you're gonna have to pay a fee otherwise they'll just discard them. But once you sell something on Thread Up it could take several days to actually get paid because they only pay out at the end of the month so you wanna keep that in mind too. The next site, number 11 is Gazelle. Gazelle's a great site if you have some electronics you wanna get rid of, some iPads, iPhones, you know, laptop computers, whatever you have what you do is you send them into Gazelle and they actually will pay you a fee for that. They can send it to your bank account, they can put a PayPal account or they can also give you gift cards. Now on Gazelle you're gonna describe your item and they're gonna give you an estimate of the value of it but if it gets to Gazelle and they look at it and they say well this is more damaged or this is more worn than what you told us they could pay you a lesser amount. As far as time investment, pretty simple. You don't have a whole lot of cell phones and computers laying around to you so it's not gonna take you much time to send it to them and get your cash and it's better for the environment than just throwing them away. And my last one on the list is Card Cell. Card Cell's a pretty cool site. Do you have a lot of gift cards lying around that you just haven't used or you're not gonna use them anymore? You can send them into Card Cell. You can send them in there absolutely free. They will pay the shipping to have it sent to them and you're gonna get your money in your account within a day or two. Now the drawback is you're never gonna get full value for your gift card. They're never gonna do that because they have to make money too. So you're gonna get a lesser amount than what's left on that gift card. Okay, so there you have it. There's 12 ways to make some quick cash online. Pretty simple. None of them cost you any money and that's great just a little bit of time. Hope you enjoyed this video. If you did, do me a favor and subscribe. Hit that subscribe button down there. Turn it from red to gray. Don't forget to ring the bell. Turn on all bell notifications so you're notified each and every time I get live or if I upload a video. I also have a free e-commerce course. If you look at the description below, it's absolutely free, no credit card required. Just click and learn. And I have some great links down there to other products. Check them out. My book list is down there. Thank you so much for watching this video and I'll see you in the next one. Hey, thanks for watching my video. Don't forget to subscribe to my channel and click that little bell right there so you can be notified every time I do a new video. Also click on one of those videos there. Keep watching on my channel.


What is going on guys welcome back to the channel critical overlord here So we're gonna be talking today about some things that should confirm more or less the Jeepers Creepers 4 Jeepers Creepers We're born is going to be keeping all of the other films canon If not alone the first movie at least based off of some prop some props that have shown up online and some other things that have come out from the film that are behind the scenes if you are Someone who has been digging you probably have already seen these things things that I of course have Again been told I'm not supposed to be sharing so I'm not gonna share them here So I'll just go go over some of the things that I have seen and let you guys be at ease To help hopefully let you guys be at ease with the fact that this movie would be ignoring anything It still could potentially be ignoring at least a second and third movie, but now we at least know or there's proof out there that The first film is remaining canon so some of the things that have come out from the set of Jeepers Creepers were born which is set to come out later this year in the fall is We have like what appears to be voodoo dolls and these voodoo dolls they have They're of like old characters that have already come and gone from the series one in particular Has a yellow band in County shirt on it and some jeans and black shoes as dairy war in the beginning Or it throughout the whole film of or at the throughout the first half of Jeepers Creepers because he did change his shirt halfway through So we have a voodoo doll. It looks like of dairy We have a voodoo doll of the cat lady with her with her gun that she comes out ready to shoot the creeper with and Then we have the cop who got his heart torn out And we have another voodoo doll of someone who lost their legs someone who had their head chopped off You know, it's just those two those three voodoo dolls of the cop They got his heart eaten out Dairy's voodoo doll and then the old cat lady with the and then dairy of course having the Bannon County shirt on That should be enough to let you guys have Some some relaxation and knowing that this film would at least recognize the first movie's existence We at least know that this should also be more proof that this film is picking up 23 years later after the events of the second Film it's not a complete full-fledged reboot That this this I just want to say this like the films in and of itself They each have their own story that they're telling anyway while being loosely connected to the next because they're in the same universe The creepers constantly focusing on a different topic in each film But they're all in the same town air an area and then hopefully down the road We'll get to see all of these survivors connect with each other. That's what we were hoping we'll get from the Jeepers creepers 3 We were promised back in the early 2000s. So We have that as proof that they're at least are gonna recognize the first film and that this is 23 years later Hopefully after the second film if they're keeping that cannon the other things that came out from on set was just footage of what looks like I guess some type of stunt double for the creeper kicking his way through a window of source Maybe this is at the horror hound festival. We know that he's gonna have his his weapons back He's gonna have his throwing stars not necessarily his throwing stars But his I'll say his throwing daggers anyway with the engravings on them that we saw in the second film those are gonna be back and I'm expecting honestly this movie to be more more action packed. I think when I say more action packed I'm just saying based off of the things that I see There's been there's footage of Explosions that apparently will be going on in the film now if this doesn't actually occur That's because they got cut out but there's things that have been online showing footage of an explosion happening this is presumably happening to his truck and This is just things that I'm looking forward to and excited about from the things that I've seen and again There's been that footage of him kicking his way through the window that could possibly him kicking his way through The horror hound festival window some type of art house or some type of haunted house that they have at the festival When that kicks off later that night or day or whenever it starts the other thing that we saw was some prosthetic heads of what looks like a prosthetic creeper face faces or something like that like how Jonathan Breck has his prosthetic creeper face that we Plenty of you I'm sure have seen if you look at the behind the scenes featurettes of the first second and third film But a lot of these face Prosthetics don't look like Jonathan Breck. There's one in particular though that does so what I want to again just say is There's no official confirmation that Jonathan Breck will be back as the creeper all we have right now is just speculation I hope he is back something in my guts telling me that they replaced him Why you would go out of your way to buy the rights to this IP only to replace and recast the creeper is Anyone's guess I don't I don't know why you would do that because I'm sure they shelled out a lot of money to get the rights for this so If Jonathan Breck is in it what I'm thinking based off of these face prosthetics that I have seen that That looked nothing like him one in particular does Not to say that it is him it just looks like him and those of you who have seen it on my social media You know what I'm talking about The other two that look nothing like him they could be people who have been Given the creeper makeup because they're coming to this horror event dressed up as the creeper Maybe there's some type of I've seen people send messages my way talking about this Maybe there's some type of competition that goes down where they're giving out the best costume There might be some individuals there who are dressed up as this urban legend the myth the creeper because we know at least now That also the process the voodoo doll of dairy Should tell us that this Louisiana area is where Bannon County is this is where Poho County is that doesn't mean that they'll be in This area, but this is at least an indication of them now saying that all this whole time We've been in Louisiana with these counties and I know someone has told me already Louisiana does not have counties again Movies don't have to be a near perfect representation of our actual world So go nuts with that if you want to but the simple fact that I'm going back to what I was saying They could be having some type of competition where multiple people are coming to this festival dressed up as the creeper and then This is why we might have so many different face prosthetics or face castings because they've chosen different people to be in The creeper makeup one of course being the real one which we're hoping is Jonathan Breck the other one being someone or two other people who Are just there because they heard about this heard about these myths growing up about this thing That's out in this farm somewhere that you have to pay to see with this old a guy with a with a son And they make a profit off of it and they just tell stories about how they killed it 23 years ago And you know because there's been 23 years since the last film So 23 years have passed that's enough time for someone who of course is would be 23 years of age in this film They've come and gone and been raised around things and heard stories about something They've never seen before but they've heard descriptions about it and now they're here at this horror hound event dressed as it That's what I'm hoping if Jonathan Breck got replaced. I have to see who they got to replace him I have to see how the makeup looks on this individual before I even say I would give them a chance I would still give them a chance. I just don't see anyone doing it like Jonathan Breck I'm sorry, and I know that if Breck is not back That's gonna be one of the biggest detriments to this entry after waiting so long to have something 23 years later Now we've lost Jonathan Breck if that's the case But let me know what you guys think about all this down in the comment section below If you haven't already make sure you subscribe turn on post notification in this video in the description I have links to my social media accounts on my Facebook tour to Instagram. You can message me there Of course, then there's any movies news or reviews you like me to cover in the future With all that in mind guys I will see you in the next video and go ahead down in the comment section below Join my Facebook group so you can message me there to talk about anything regarding Jeepers creepers or just post anything horror related