Hello everyone and welcome back to another video. So in our previous video We were looking at power automate and how to create a an automated flow so that every time an email was received You'd be able to store that or the attachments on that email into a share point document library What I'm going to do is just continue on from where we left off Just had a couple of questions about this were all valid points But also I wanted to cover off this element of adding some logic or a condition into this For your scenario so what we've got the moment and if you haven't seen the previous video You might want to go and watch that now else. I'm sure you can probably keep up We've got our flow here and we can see that we've got this current apply to each Attachment and basically all it's going to do at the moment is regardless of what the attachment is It's going to save it into our SharePoint document library What we've also got open here What I want to do is add in some condition or add a condition in here Which first tests what the name the attachment is so for us we're working with a staff upload So we we only really want to capture an attachment called staff upload from the email If there was other attachments attached to that we don't want to save those So this is why we're adding this condition in here to ensure that the others are filtered out And we only save the one we're after so what we're going to do again Let's say following on from where we left off. We're going to first Well, this might seem a bit of a reversal order But hopefully it'll make sense of why keeping this and we'll delete this bit out later on What I'm going to do first is add an action and That action is going to be a condition which for us has fortunately been up at the top of the list already But you could search in the search box to find it if you don't and when it pops up You'll see you've got this condition box here and also the if yes and if no so basically If we'll add a condition in here, and if that condition is true, obviously it will do the yes on the left side Else it will continue following this no option over here So what I want to do first is I'm going to go Into here and I'm going to say okay The first thing I'll do is I want to do a test on Attachments name so attachments name here is the same as the attachments name We're using here in the file name. So this is going to be the final name of each attachment So is equal to and I'm only interested in it's called staff upload dot CSV So if that's if that condition is met then what we'll do is we'll do this step So I'll let this open just for a cheat sheet for me to a quickly bad to enter back in So we'll go add an action here, and we know this is going to be called create file And we're looking for the share point option. So we just need to select our SharePoint site the folder path for us So we click this folder icon is going to be in the test archive and daily email to on that folder And then when it comes to the name of the file all I'm going to they should do is cheat sheet and copy everything from here So the first thing we want is a attachments name, which I can scroll down and find here Attachments name and obviously the content of the file is going to be again the original attachment content So we can see what that option available to us here as well And all I just need to do is add this little dynamic text we've got in here There are ways that you can do this so that You can do this in a condition and then you're able to reference it in each element what I'll cover in another video But for today, we're going to be sticking with our not to call it lazy, but yeah, maybe our messy solution So I'll take that go back into here to the start and we'll go into add some dynamic content It's gonna let me up. There we go expression Let's you're gonna put paste it in there and I want this to be at the beginning So the easiest way to do that probably is to read attachments name in here Just for reference what we're doing is we always want to retain the attachments name But we're just putting this date stamp at the beginning of it with an underscore so that as multiple days go on You have a date stamp as it's cool So you know which what each day refers to it just helps to keep everything tidy So now that we're regret run requested Replicated this element back down here. I can now delete this if I was to run this at the moment What would happen is it would create or save a file for each attachment You then do the logic and then maybe you would duplicate it or obviously if it wasn't met and not do it So I've got it down here. So let's just delete this one now Yeah, we definitely want to delete and now we can see a bit more about what's going to happen So when an email comes in what it would well just to be a bit more detailed as we know there's a bit of criteria here We're only looking at emails if the subject filter has daily attachment And then if that is the case what it's going to do to every attachment in that email regardless if it's only one Attachment or maybe 10 it will loop through each of those attachments And that's being done here by apply to each and it's then going to apply that this condition It will say right is the attachment name equal to staff upload if it is it will save it in our designated folder If it's not then at the moment It's not going to do anything because we haven't defined it to do anything here at all So happy of all that when I'll go down and click save And once it's happy yet, we just need to test it so we'll go back into our test option here into manual and go test So we're now in our inbox just to send ourselves a dummy Dummy email so that the workflow will will test it to make sure it's working. So just send an email to myself I've got make sure I've got the correct Subject line in here, which for us is daily attachment and Then I'm going to put some attachments So for this example, I've got three different attachments one is staff upload which we want to capture and then we've got a couple of Plug one so one called not staff upload and everyone called staff upload dash copy They've uploaded so let's hit send and we shall see what happens. So yeah, we can see the email has been received So let's go back into our flow for a second So this should now acknowledge that we'll see an email has been received into that inbox and then trigger The flow that we have associated for that Which should hopefully need to take a couple of seconds and there we go. We can see it's worked We've got our green ticks against each section to show that it has ran and obviously the useful information here to show Duration is taken. I guess the main reason I'd or more important side look at this If we were used to it being two seconds and suddenly it's gone up to 10 seconds That obviously indicates where you might want to maybe improve your workflow to obviously save a bit of time But nonetheless, it's all just useful information and then if we go into our share point We can see in our daily email folder We have got our staff upload attachment and we can see it's today's date So we've got the 25th of August here and our staff upload and this is the only file that's been uploaded So there's other two file names because they didn't meet the criteria. They have been excluded from Obviously being saved You might notice that or remember that one of those files was called staff upload dash copy So you can change your logic so that it and it says when it contains rather than when it's exact So I think I could probably show you that So yeah, if you go into our condition, you can see at the moment It's attachment has to equal this entirely. We could change that and say oh We only were interested if it contains so as long as it contains the word stuff upload somewhere that would work But obviously for us to be specific. We've put it is equal to stop upload What you could do here and what and probably is a good sort of way of working if you're dealing with this is you could create another Sort of catchable section here.  So we sort of start from a blank canvas as such Okay, so what we could do here. Let's add an action into no and We're going to do create trial and The site address is going to be the same The folder path is going to be slightly different. So let's go test archive Daily email and this time we're going to select other and Then I'm going to copy everything else into here again very much the same So again, I'm being very lazy here and just going to copy out this concatenation I've got good dynamic content and Then I'm going to use attachments name and then this last one is going to be attachments content So what we've done there is ever so slightly updated But it's paid maybe a good way of working Just think as a catch all for any of attachments So all this remains the same in the sense that if the attachment name is equal to staff upload CSV then great it's going to carry on and it's going to Save our target attachment to a given location However, if there's other attachments on that email, maybe Later down the line is decided that another attachment is going to be included as well as or maybe It could be any number of reasons. I'm really just trying to make some up the top of my head But if there is another attachment on this email this there isn't a staff upload What it's going to happen now is it will say well, no, this condition is not meant met But what I want to do is capture that attachment in an other folder, which we defined here so we'll just save that and We'll also test this new updated flow as well just to see how that's working. So we'll go into test manually go test and Like it's now instructing us go back to our email and get another dummy email made So once again, we'll send this to myself and we'll call it daily Sorry, mixing my words the subject obviously has to be daily attachment I think I mentioned in previous video that you don't have to do filter just based on subject You could run this on every email that is received from a certain inbox or person and then obviously apply A criteria to that daily attachment for the purposes example is just a lot easier for me to do So I'm going to just drag our free attachments back into here again to run our test They seem like they're uploaded. Let's send that email Perfect that's now come through Let's go back into edit your workflow. We can see our little Loading icon is here. So we'll just let that run its course And obviously execute the flow and see what it does with our attachments perfect. We can see it's now done Obviously this This is this section has taken a bit longer. You may remember it was previously two seconds But it's now four seconds that obviously we don't want to take longer than needed But it's a good indication that obviously it took a bit longer because it's now applying logic to those additional two files So one thing to bear in mind the more attachments on an email obviously the longer this flow is going to take to run But if we now go to our SharePoint site We can now see that as expected as we have previously it saved our staff upload file Dated today into our daily email. However, additionally now if you go into our other folder, you'll see we've got those other two files Also stored in there Just so we've got them for reference. Should we need to or should we want to catch them for whatever reason? I've obviously put the date stamp on there from as well game just good practice so we can identify when those files were received But obviously depending on what your criteria is you may or may not need to do that So I hope you enjoyed that video and you found it useful and maybe even answered one of your questions You had around a power automate Flow if it did, please don't forget to give this video a like and not only greatly appreciate by myself But it does help that all-important YouTube algorithm and also if this is your first time maybe finding one of the videos Or you've watched our videos before again Please can I ask you to subscribe to the channel and hit that bell notification button that way you'll be Notified of all of our future videos as they come out and of course as always if you have any questions about the content of this video Do just drop a comment below this video and I'll try to get back to you as soon as possible Thank you very much for watching and we'll see you in the next video


The following is a presentation of TFNN, the Tiger Technician Hour with your host, Basil Chapman. Call now toll-free at 1-877-927-6648. Good morning everyone, Basil Chapman here on this Tuesday the 9th of April we're looking at the Dowdown 63 at 38,831. One of the reasons why we've remained short the Dow, long-term we've got those long positions we're not touching that at all but as a trading vehicle and there's no time on this as a short-term trade it could turn out to be longer term but we starting it off as a short-term trade. At 39,889 the Dow the 21st of March made an all-time high it pulled back and then it bounced and it went like 20 points below and I consider that to be the equivalent of a peak D so I have a technique that's called Chapman methodology we try to identify a buy signal that can get upgraded to a buy mode and when that buy mode is in place it should go to at least four higher peaks. Peak A is the first peak, P is the second, higher peak will be C and then a higher peak over that will be D. It can even go E, F and G. I've got techniques that say at D other things can happen but the most important thing right now is that that double top saw technicals that were fading and look at the MACD and the sarcastic but that nine-period moving average was absolutely determined to hold until it was the last Thursday we had the big smash to the downside of 500 points and that turned it the nine-period moving average peak. Now why do I make a big deal about this because this one technical indicator look at this from March from November the 3rd it's the nine-period moving average went positive and this one indicator kept the Dow in a buy mode right up until this very moment when it turned pink. I mean that's so it goes all the way to what date is that that's on the 20 on the 4th of April so you've gone from the 3rd of November to the 4th of April I mean that is that's amazing right and now it's turned pink so that to me says it's a signal it's a sign it's just yeah it's 30 stocks but they're very important stocks so let me do this I'm just going to see aha we've got Garo in Newport Beach California Garo how are you how are you sir? A little scratchy but otherwise good. Good, Mr. Basilev, if you have the time if you look at KOLD it's a reverse of boil or you know NG. Yes. I shorted that last night at the close of the market because the 60 minutes was showing bear and the daily chart was bear the daily chart has six dots at the very top today it dropped drastically here I have a question from you sir is by looking at the daily chart and the weekly chart do you think this will go down as 117 dollars and 32 cents which is at the high of the December 13 23 the high of the December 13 23 was 117 32 and if you look at the weekly chart that you will see that that price is right on the 21-day simple moving average I want to see that and but of course this is going to split this stock is going to split 2 to 1 on April of 11th I mean day after tomorrow this is going to split in half it's going to be around 60 dollars so this is another information I'll listen to you sir. So I also have a question for you so the UNG will also split it'll be a reverse split it'll be going from 15 to something bigger than that is that do you know? No UNG split for 1 to 4 reverse split on January the 24th. Oh no I'm sorry. Yeah 1 to 24 that's split now it's January the 24th yeah but no KOLD will split 2 to 1 on April the 11th. Okay so let's do this right now what we're looking at here is that for the very first time since it crossed positive KOLD which is the let me just give you the exact time this approach is ultra short there we go Bloomberg natural gas. Yes sir. So this is an ETN probably so it's at 133.18 it's down 557 so you went short last night so you were short in the 100 what 130 940 area? I shorted at 138.91. 138.91 okay and it's now at 133.10 so the 9.3 this is a this is a daily chart and the daily chart as only now seen the 9 period moving average crossed under the 14 period moving average so let me go to the 120 minute chart because your question is basically because it's under that left side low of four days ago how deep do I think that this can go and the answer is I was actually looking at UNG early very early this morning I can't remember this morning or last night and the UNG which is the this is the one to I guess I think it's one to one United States natural gas fund made a peak made a peak F pullback sharply and then had another higher high and it looks to be like this is actually a leg C so let me just put this in the notation yes that you know exactly what I'm talking about because this will be kind of the root of what we're looking at is where this goes that the other one is going to respond that's what we have to have the choice sorry to cut the short I'm so sorry my apology both boi L is the double of that if you want is the double yes it's a double and KOLD is the double reverse yes sir sorry to cut your short no that's good so now what I'm looking at I've got this as a leg C I could make an alternate count I don't need to I think the MACD is good sarcastic is getting close to 80% to 70% on balance volume is good the 9-period moving average for a few days now that's one of the things I was looking at I didn't do any notation here but I was looking at and thinking okay that's a good sign that you've got the 9-period moving average moving up in the UNG so now let's go back to the story so on the UNG the resistance that I'm looking at is in the daily chart the high that was made the little pop up and then it failed on the 12th of March and we know that failure has been the norm for UNG for a year and a half a long time every time you thought it was going to rally it just failed so this high of the 12th of March of that's at 8 let me get this exactly right 15 yeah 15.20 and today's high so far 16.08 is that correct am I right yes no 16 16.08 is the high that was made on the 12th so we've just tagged that so what you want to see I would base it on the UNG because that's you know where gas is going is where the ultra short will go in in deference to this the strength so so far it's a nice gap up and normally I don't have to tell you you've probably watched this almost every gap on the UNG just a couple of rare ones back in February failed and they closed the day at the low of the day so I'm going to get if you don't mind have you got time I'd like to just go to my 10-minute chart because this what happens over the next hour is going to be really important so if you can hold on we'll get there okay absolutely okay we'll be back with Garo and Newport Beach looking at we're looking at cold KRLT that's the inverse of natural gas down 25 S&P's up four we'll be right if you're looking for potential trading setups in the stock market then rocket equities and options report is a newsletter you should try Tommy O'Brien delivers options and equity trades when the markets present them using a combination of fundamentals and technicals sign up for rocket equities and options report today with a 30-day money back guarantee so you have nothing to risk for all the details and to start your subscription today visit the front page of tfnn.com tfnn educating investors in the world of trading only a few names stand out like Larry Pesavento a pros pro with over 50 years of experience Larry has seen it all a former Chicago mercantile exchange member Larry has authored 10 books and trained over 1000 traders with his unmatched expertise introducing Fibonacci 24-7 Larry Pesavento's daily trading service that turns the complexity of markets into opportunities published every Sunday receive a comprehensive report packed with detailed commentary charts and videos that illuminate the patterns shaping the markets with updates 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gold report newsletter now at tfnn.com tfnn has launched the tiger's den hosted at discord tfnn has been educating traders for more than 20 years with live programming hosted by a variety of professional traders during market hours the tiger's den available to all tigers and tigeresses for just one dollar for the year there's no catch or added costs when you join our community of traders sign up today and become a part of this educational community of traders just visit the front page of tfnn.com toll free at 1-877-927-6648 internationally at<PHONE_NUMBER> hi folks we're back so gerald before i forget what i wanted to say earlier on and because i was so busy looking at the charts is that the commodities are running natural gas is in a different category altogether but if commodities run they kind of grab all the others and i think that natural gas is actually now it has i'm just saying it has the potential it was the first time i'm looking at it and saying this move that we've just seen over the last day or so um has the potential if ung is able to clear 1622 kind of get into the 1620s preferably today not tomorrow i'd like it to be today if it does that without taking out 1560 as key support i think you've got something that can actually last um let me just go back to this on the daily chart yeah i think it can actually last a couple of days and there's another you know you kind of get out of it and a certain point you just okay that's it and you're out but the way i'm looking at it if you want to take some money off take a little bit off now but i would have a trading stop on this because the way it's looking if it clears at 16 let's call it let's call it a 1612 area if we can get into the 16 high teens and then get into the 20s preferably today but maybe tomorrow then all of a sudden you're looking at a beautiful cup formation and that's the exact inverse of your your cold which is looking at i've got a cold right now but this is a difference with the k so and also the days young if this s lasts through tomorrow that'll be two days of the nine-period moving average going under it i think you've got some momentum to the downside and that means you a natural gas for the first time can actually extend its rally i hope that helps you yes you're absolutely right yeah i see that you and g the 50 day moving average is 1622 you're absolutely right even if it ends there he has a lot of work to do the fine day has to come and cross that 50 day if the five-day does not cross the 50 day this rally is going to end up very fast and this is going to roll down back again to 15 dollars which it just keeps doing all the time so i would just say to you um i'd have i'd have i do money management just like you do i at this particular point because it's had a very nice move it's giving you a nice profit i take a little bit off but at the same time the way it's acting with a and you've got your saw confirming you've got for me the nine-period moving average so far um it is in the daily in the 120-minute chart already quite established and now it's gone with a dreaded h so far it's gone under the left side low that's that pattern that we look at that says if in the arch formation it fails at a peak a or a b and takes out the left side low and then closes two bars underneath that left side low you can have a very sharp move to the downside you can have almost a one-to-one extension i don't know if that's the case here that's a little exaggerated but normally you'd see like a one-to-one of the arch high to the base and that's uh you know that's 147 and i wouldn't put it that far you want to go one step at a time even as we speak i'm sure someone's going to try to abide you and g but i i think the weight of evidence is saying that there's a chance that you and g this time breaks a little bit to the upside mostly because of the commodities hope that helps you yes sir yes you know i'm i'm i'm fine i'm i'm going to listen to you i made already four thousand dollars on that kod wow that's great and then i wait see that's what will happen and i go accordingly if i find any problem i call you tomorrow day after tomorrow by the time you very much for your knowledge and information have a night thank you for calling and usually have a great day and congratulations so so folks i just want my i wanted to talk about um so i just wanted to go through these quickly here so dowel kind of fading sort of coming back is only now it's 15 right now let me just have a cup of tea there we go um but the pattern itself you see this arch formation that's a repeat c1 and c2 what i was saying before i took a girl's call is that a c2 just underneath the previous c1 acts like a d and it can pull back very sharply and if you get c1 c2 c3 because it just keeps making slightly lower highs that says you're kind of using up your downside power and you're probably going to make a big spike to a d and then come back down again but that's not the case here what is the case is that the mac d is very weak in the dowel the stochastic is down to 21 percent i suspect that we're going to go to the the single digits at some point i'm not going to say soon but i think in fairly short order there's a chance that we can do that that'll be a big deal so i wanted to go through this just to show you how i'm looking at the market s and p holding well but kind of running out of steam have a look at this chart on the left you see this up channel i took it out of the dowel because i just but that's already gone through but in this case this is still an active channel it's a look it's right in the channel it's holding very well so as long as it's holding that's fine but my eye says that the technicals are starting to fail in the dating that's the mac d the red to strength the gray line let me have another tea i hope i don't lose my voice uh huh good and the 120 minute chart you can see just stuck in a sideways range excuse me and i'm coughing and the pink night period moving average is still pink it's not switching to green so it just says be a little careful the qqq i don't want to do this through the whole show so let's hope i can stop that right now made a higher 449.34 about three three four weeks ago three weeks ago and the peak d in the weekly chart and all the technicals that count in the weekly chart are saying with that nine period moving average so high above the 14 it's going to be a whole process or a downside sudden smash that gets that green nine period moving average to go pink so that's all very positive even though it's at a peak d in peak d's where other things can happen it's only a leg b in the monthly chart so that's the qqq looking out for very strong short-term start to show a little bit of deterioration but that nine period moving average now when you just go to this chart right here because this is you've got to see it for real look qqq look how the price is going up but the nine period moving average is pink but very narrow it means that look you can see it it could flip to green very quickly if the 441 85 right now changes to 440 what's the height today 443.24 if it goes to 444 by tomorrow this time let's make it after the fed speed let's say 2 30 tomorrow afternoon if the qqqs up in the 446 447 area that's a good sign but if they're down the 437s that pink is going to move and you're going to forget that IWM the Russell 2000 which is holding okay right now up 85 cents so here it is that green is getting so close to the nine period moving average turning pink it's just but it's done it before and then he had a big move up but it's also using up too much time sideways so that's the reason why I'm saying to subscribers we're watching this very closely I'm anticipating that we're going to add to the Dow short position to to other indices or other stocks in fact there are a couple of stocks that I'm looking at that are starting to show deterioration so I'm I'm quite ready to and on the short term to look for some downside action I'll be back in a moment down to 10 if you spend any time online researching trading techniques on how to begin your trading journey you've no doubt come across many folks who push forex trading as a way to make big money quickly unfortunately there are equally as many stories of these so-called forex professionals just looking to make a quick buck off aspiring traders without actually teaching the ins and outs of the forex market this is what sets teddy kek stacks the tiger forex report off the riffraff every monday former chicago mercantile exchange member and author teddy kek stat releases his tiger forex report newsletter where he dives into the complex world of forex and takes time to actually teach you his methods that have made him so successful in the fast-paced and rewarding world of forex trading furthermore all subscribers receive access to archive live streams of teddies where he provides university level education to help you in forex trading all first-time subscribers receive a 30-day money back guarantee so what are you waiting for forex awaits many trading newsletters attempt to focus on a narrowsef equities or commodities while this works for some it oftentimes misses many opportunities that possess huge gain potential but how is an independent trader supposed to scan the entire market looking for these hidden opportunities one simple answer the opening call newsletter basil chatman developer of the chatman wave trading methodology has been trading the markets for longer than most trading influencers have been alive and over that time he has honed his methodology in order to accurately call movements in a wide range of equities from semiconductors to uranium to key indices and so much more basil is old school taking the time to educate the trader while also giving his insights into key indices selective stocks and more opening call subscribers also receive access to dozens of educational live streams that can be accessed at any time for your edification all first-time subscribers receive a 30-day money back guarantee so ignore the pop trading influencers and start learning time tested technical analysis are you ready to take charge of your financial future tfnn is your gateway to the world of trading and investing whether you're starting out or scaling up tfnn empowers traders and investors of all skill levels with top-notch investing systems strategies and techniques it's time to protect and grow your money with insight you can trust join us live monday through friday during market hours for exclusive content that moves with the markets at tfnn we bring the trading floor to you our season hosts are here to answer your calls and questions live on the air check out the tiger's den for just one dollar and follow us on youtube and become part of our vibrant community and remember at tfnn we're so confident in the value we provide that we are for a 30-day money back guarantee on all new premium newsletter subscriptions and services you have absolutely nothing to risk so why wait tune in live to tiger tv and transform your trading journey because when you know better you invest better join us and experience the difference today tfnn educating investors this portion of the tiger technicians hour is brought to you by directions daily leveraged and inverse ETFs whether you're a bull or a bear you choose the direction visit direction.com investing in the funds involves significant risk and should only be utilized by investors who understand the impact of leverage and actively monitor their portfolio they are not designed to track the underlying index or security for more than a day before investing carefully consider a fund's investment objective risk charges and expenses contained in the prospectus available at direction.com read carefully distributor foresight fund services LLC hi folks we're back so you're looking at the SMH is the semiconductor index down to $1.82 221.36 on the daily chart right here you see this this on balance volume has given a couple of clues to turning down almost at the exact highs and then and then at the low gives a nice balance but that's not the issue the issue is you see how big and red it was look if you're just a moment ago an hour ago it was green up at the top and now you've got the the candle big red candle makes a high of 225.95 trading right now 221.22 I suspect that this is going to turn pink and that means that the that the nine-period moving average is giving its first look at this I mean it's hard to believe since a two-day or one-day reversal back in January the fifth and sixth fifth and eighth there was a weekend Monday a Friday Monday that's the only time it's gone pink since it turned up that was about the sixth I think yep the sixth of November so look at the nice spacing between the two and this one's gone on for much longer and you start to make lower highs and lower lows I'm kind of I I'm looking at this and I'm suggesting to you that the the act of uh let's see the the semiconductors lead markets up and down they are the chip sector and chips are the oil of the 21st century the crude oil that is but look at this Nvidia has gone pink this is the first time uh it's gone pink since it went green back on the 13th of December so I'm I'm respecting this and I'm saying that I believe that we're in the process of making some kind of a top I don't know yet what it is I mean if I look at some of the charts look every time you look at gold you think oh I mean come on really it's got to turn down it's got to give you an opportunity to do something well look at this it's up $30 a $2,381 so you don't always get signals to comport with your with your uh anticipation your your own thinking because the price is the price it doesn't know what you're thinking it just does what it has to do so if you look at the SMH's you go one by one lost that I went through I mean once micro devices the AMD it's been pink that means it's been in cell mode for quite a while it had a 227 all-time high um and that immediately was followed with the round number 205 now I talk about round numbers look at this the Nvidia had a round number all-time high of 974.00 you know I I've spoken about this and I said I although I don't I've never seen this I've never maybe I've seen it but I've never calculated the numbers of round numbers that I've seen uh this is the the vast number it's just I I've got them written all over I don't want to go through the wall but all what I do want to say is I believe that it is hysterical buying it's fear of missing out I mean why would you just say I'm going to buy this at just give it to me at at uh 1,000 what was that one uh mstr give it to me at 1,999.99 it was the round number 2,000 by one penny but that very day that uh micro strategy made its all-time high a leg deep oh cough cough cough in the daily so in the monthly a peak f it was became a peak f in the weekly chart that peak D on the uh beginning of April had the same day it had opens at 1953.00 it had a 1999.99 and how unusual is that all-time high but that same day it closed at 1942.00 as a round number low and is trading right now at 14.01 I this to me is it's really important and that's why I'm saying I'm just we're being careful we have actually added to the long position today but it's in the financial sector and I think the financial sectors especially one part of the financial sector I think has the chance to be uh kind of the underdog that just kind of holds I don't know yet we'll see we've tried it before we're going to try it again and that's the way I'm looking at it so just now I need to finish some other things that I had caused about it was also about Tesla I think that Tesla trading right now up 3.88 at 176.95 I would never rule out Tesla as a formidable force that has the capacity to do a lot of things that has nothing to do with the actual automobile itself that has to do with energy batteries a whole thing but on a purely chart basis if at any point in the next week it closes under 165 it's at 176 right now that's 11 points away but if it closes under 175 I would not be surprised to see a one-to-one to the downside of the move that was just under it said it's called 184 to 161 so about a 20 point so that says 175 yeah you could move to the 155 area quite quickly and if you're looking at the weekly chart this little cluster every other week looks like oh that's good oh that's bad oh that's good but it's just stuck in a range right 185 to the 160 area it's just stuck and the monthly chart night and all of them have nine period moving averages that a week the daily one is trying its best to turn green but it hasn't yet but if you're looking at the MACD and the stochastic they are way under the on balance volume so I think Tesla although there could be these sudden surprises a bit of news or whatever it is the trajectory that I have is that it's making lower lows and lower highs but it could start to form how it handles between 160 and 157 I'd actually say is going to be really important over the next three to four weeks and I think it's going to be challenging that level so that was a question what do I see for the upside on a short-term basis if whatever is as I say this is a company that always comes under a sudden news if it grows over 180 closes over 187 I think that's slightly different in timing it means you could stall for a while before you come back down but that 200-period moving average of 198 in the weekly chart has been such a formidable repellent zone and it hasn't even got close to it hasn't even crossed the pink nine-period moving average of 180 so I think it's a work that it's a it's a chart pattern that says probably just going to be making lower highs and low lows for a little while longer all right next question came in let me just see if it is here yeah boy this two times natural gas we're not looking at that Bowser can you look at RTX oh I didn't skip anything but this is from Tyga YouTube yeah RTX is um this is Raytheon and Raytheon is the defense sector it just made a high uh normally one time high but made a high yesterday in 102 area 102.20 second straight down the dollar right now at 100 this is a very good control I'll be back in a moment gold report as a precious metal gold is still king it continues to hold the most effective safe haven and hedging properties across the global major trading hubs of the London OTC market the US futures market and the Shanghai gold exchange the gold report Tom O'Brien publishes his weekly gold report every Monday morning for subscribers consisting of coverage of the XAU hui gdx the dollar bonds the south african rand as well as 25 different mining equities with specific buy sell recommendations the gold report new subscribers get a 30-day money back guarantee so you have nothing to risk subscribe to Tom O'Brien's gold report newsletter now at TFNN.com the stock 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services LLC this program is brought to you by Vista Gold traded on the NYSE American and TSX under the symbol VGZ Hi folks we're back and I had a question about galaxy digital it's BRPHF trading at 9.7827 down 2.05 trades of those extra numbers because I guess it goes as an over the counter stock it's just making it making a peak D today it made a peak E before then it pulled back in the daily chart from the just over 11 to the nine-ish area and now it's just popped up to 12 and yesterday and today it's down at 9.78 I think it's going to test the lines this is the cup formation the right side is a little bit weaker than the left let me show you the technique that I use look at that that late April February high and then the high yesterday look the MACD was much weaker the on balance volume very weak the stochastic try to get to 80% and then failed is pulling back it was way way stronger there and the 9 period moving average was very strong now the 9 period moving average is still very good so I'm not going to I'm not going to dismiss that what I am going to say is that the parallel move of the green and the black lines in the same trajectory down whereas if the black the 14 period moving average was holding steady sideways just horizontal but it's not it's pulling back tells me that there's a good chance that nine is going to be tested but it's a leg F repelled at the 200 period moving average in the weekly chart it hasn't even touched that level since way back in 2022 right about April or May it went to the what is that probably about 17 or so before it tumbled down so as it stands right now this is a the fourth or fifth test of the 200 period moving average it should come back to test that at some point but right now needs to pull back and you can see 9.23 is the 14 period exponential moving average support level it's not actually been there since it broke upside on the 9th the week of the 9th of February and that was from 8.6.75 up into the 9s so I think it's coming back digesting some gains hope that helps you next question is a volatility index the VIX index right now I don't like it when the VIX index spirals higher just a day after some of the markets are making highs well some of them have been pulling back that SMH has been pulling back for a month so right now down to 81 the SMB is down 34 you got a big green candle now I always put the chapwave notation in the VIX index but it is meaningless I just do it because my eyes says you just better do it but in fact it doesn't mean anything it just I look at price more than anything else and it's interesting that so far we've had a number of ease and these but as I say that's not the most important thing that doesn't mean to say this is a peak C there's a guarantee you're going to go to a D in the volatility index it does happen but that's not the methodology that I use this is the only index that I just say forget about the chapwave notation although put it in what's really important is that the velocity to the downside and this is what I thought we get maybe tomorrow the fact that it's happening today means that if you're down let's just let's just say for imagination just here we go at the end of the day the Dow is actually where it is right now down 264 SMB is down 32 and then tomorrow there's really good news what you've got to make up almost 300 points in the Dow so getting the exact turn is really nice when you can do it fortunately for us with it with the Dow we've we've had this position for a while and we've had a lot of leeway because we timed it fairly well however within the context of getting into different short areas right now you have the risk that they could be I mean let me just do this so the volatility index holding at 16.46 if it holds over 16 for the rest of the day I think the selling pressure will remain not sure about the close yet the higher the VIX goes the greater the chances are they will get a much sharper decline at the end of the day but look at this the TLT is up 73 cents at 92.11 it made this arch formation let me just draw this in right now right here 92.01 was the loss was the low of February getting scratchy just a minute oh did I have been finished the art of the Raytheon I don't think I even finished it so it's a nice little bounce this little cluster formation in the yields by in the TLT you'll see the same thing in the yields look so here we are made a peak E in the ultra short Lehman 20 a treasury bond ETF but here's the issue the issue is that it is holding quite firmly over there the ninth over the 14 the MACD is good the stochastics is 79 almost 80 percent the blue unbalanced volume is good so it's going to be what the Fed says and what the Fed does and I don't see that the Fed can really talk about that I don't think they even want to mention the word lower rates that rather be looking at what we're looking at we're looking at data and so far this and so far that so it seems to me and now you've got the commodities going higher you've got gold still up sharply up how much 14 coming back a little bit I wouldn't be surprised if gold is just getting ready for a little bit of a pullback and it's easy to say you could say that every single day and be wrong but it is in late D these where other things can happen and D in the daily and D in the weekly and D in B in the monthly but look at silver silver popped in 28 to 44 it's a 2797 right now let me just check what kind of candle we're getting right there yeah okay a little skinny candle we'll see what happens at the end of the day it looks I mean visually it looks overboard right the unbalanced volume is extremely overboard if buying keeps coming in we're going to see good support in the 26 to 25 area so we're looking at that so this is what I'm saying fcx just screamed even higher today it went there you remember the other day I said Freeport McMorrin made a peak g-c and I suspected well almost all of these that do the Chapman with instant restart eventually go from g-c to a D and then you've got to be careful well lo and behold there's your g-c oops sorry I didn't mean to do that this is the one I meant to do right there there's the inside all right okay okay at D so now you've got an instant restart that means E-A F-B and I type in g-c I said the rule now is always say g-c when you get to a G and there's your D and that makes it a little bit vulnerable right now Freeport McMorrin fantastic all the time high well we'll see because it made a peak right here back in May of 2022 a high of 4141 and today's high well let me just say with a high of 4141 if you're trading at 50 right now having hit 52.03 you're definitely making it all time high so that finally gets you to D even though this is peak APB um yep this is not just a C that's that's C gets weak uh it inaugurated comes right back again and now that becomes your D you always want to be ahead of the game with the chaff and wave notations and there it is so as it stands right now I think the Fed is at a tough time I've got a break coming up I need it get my T already I'll be back in a few moments that was done again are you ready to take charge of your financial future TFNN is your gateway to the world of trading and investing whether you're starting out or scaling up TFNN empowers traders and investors of all skill levels with top-notch investing systems strategies and techniques it's time to protect and grow your money with insight you can trust join us live Monday through Friday during market hours for exclusive content that moves with the markets at TFNN we bring the trading floor to you our season hosts are here to answer your calls and questions live on the air check out the tiger's den for just one dollar and follow us on youtube and become part of our vibrant community and remember at TFNN we're so confident in the value we provide that we are for a 30-day money-back guarantee on all new premium newsletter subscriptions and services 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investor you were born to be tfnn educating investors don't forget you can listen to tfnn live on your mobile device 24 hours per day go to tfnn.com then hit watch tiger tv that's tfnn.com then hit watch tiger tv that's a question came about the signals that i had earlier on in the uh one minute five minutes and ten minutes uh e-mini chart sb yuppie gave you a peak deal right at the high of 52 74 i think it was 25 maybe right there pulls back very sharply that was a peak g and i put in the arch formation with the test of the 200 period moving average which it did and now it's way below it same thing at a peak d in the five ten minute chart so that yeah that's yeah that's a nice turn around but the most important thing what we're looking at you as we wrap up i want to just show you this look the smh's and to me it's absolutely key smh's are really kind of the the bell weather these are the guys this is the one they call that the the the ship that the icebreaker right so if in fact the semis start to pull back and you get by tomorrow the next day that nine period moving average turning down and look you've already got it i mentioned that i showed it just now in video with a round number high here it is down sharpie down 40 points i think we've got something going on here that is a little bit of a surprise to the people that uh i'm considering that this is a bull market that will have no pullbacks and just keep going to higher highs i think this is a breather a very important breather so they just get that out the way the other thing is look at how what happens now with the tlt is going to be very important because it looks to me like just from the chart formation i think the yields are going to have a tough time really going much lower they might just stabilize sideways but this is going to be very important so what happens tomorrow at 2 30 or 3 o'clock is going to be very important for economic news related things as well as just purely technical chart patterns so the upside i think is kind of limited just at this particular point you've got to be careful so as i say we did add something today but we've done it in a very specific way we're raising raising stops on on the long positions um be careful that's all simple just be careful don't go crazy just be careful have a wonderful rest of the day i'll be back with tom over the later on thank you for the great programming see you in my


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Hi everyone, Joe for jaspyscasewrakes.com coming at you with our final Prism Basketball Salo Pack break number 168 to sell out Flawless giving away the final six random number block spots Coming up after this will be those random number block randomizers and then the break itself in another separate video At the end of the break re-randomize everybody's names in a second dice roll top three get into the sixer's number block The next three get into the magic number block both both of these number blocks feature multiple teams big. Thanks to All of you here. Thank you And all the teams are in and let's roll it randomize aims and teams at six and a four ten times one And 10th and final time Ten times of the teams nine and 10th and final time. All right. Here is the first part of the list Right there second part of the list right over here, and let's just print and rip Wands over Killian Hayes in the back. We're about some red, white and blues Ilya sova Seth Curry and a James Weissman red, white and blue. It's not too shabby. That goes to Steve Birch and the Warriors All right, let's flip back over here. Let's gather everybody's names Let's do What we came here to do give away those final random number block spots So let's roll it and randomize that list Five and a two seven times top six after seven. Good luck one two three four five six and Seventh and final time after seven from seven on down Sorry gang. We'll get him next time Top six you're happy though. These people are sad sad times sad faces But happy faces for Anthony Shawn and Steven David Andrew and Steven So after seven times you are in these number blocks right here. So Steven Andrew and David In the Sixers number block Steven Shawn and Anthony in the Orlando Magic number block and we'll put Mosaic one six eight next to your name so you know you can you know, that's where you won that spot I'm Joe for Jasper's case breaks.com. I'll see you in the next videos for the number blocks And then finally flawless Jasper's case breaks


All right, so thank you very much to everybody for joining this talk on real time machine learning with Python. There's a lot of really interesting content that we want to go through. We're going to be covering quite a lot of topics as we go through, so please hold your seats. It's going to be a very interesting area that I would also love to generate the discussion even after the talk to continue exploring best practices. So you can find my Twitter through this link that will be in the corner. I'm going to be publishing the slides and the code later on today, so if you want to get them you can access it through my Twitter and my GitHub handle is also the same. You'll be able to access them through there. So a little bit about myself, my name is Alejandro. I am the engineering director at Selden Technologies and the chief scientist at the Institute for Ethical AI and Machine Learning. I'm going to talk a bit more about that in a second, but primarily my background is a practitioner, sophomore engineer, across hands-on and leadership roles, currently leading a set of teams for open source machine learning. And more specifically, my role at Selden and Compass is our open source production machine learning deployment framework for Kubernetes. We focus primarily in the life cycle of models once they have been trained and all the complexities that come around productionization of machine learning. So a lot of the things that we're going to be talking about today, it will involve the best practices that we've identified when performing real-time as well as just general large-scale machine learning deployment. Although today we're also going to be diving a little bit into the training side. And also my role at the Institute for Ethical AI and Machine Learning, this is a research center that focuses on developing work towards responsible machine learning. This is primarily through contributions to ongoing standards, as well as working groups that are working in this type of verticals. Recently, one of our contributions was to the Association for Computer Machinery, the ACM. We published a statement on contract tracing applications, primarily towards the technological implications and best practices. So if you want to check it out, please do and any thoughts are appreciated. And the Institute is also part of the Linux Foundation. We're quite excited that a lot of contributions are still in the intersection with open source. But today we're going to dive specifically into conceptual introduction of stream processing and just generally streams. We're going to also be talking about machine learning and machine learning in the context of real-time processing, as well as some of the trade-offs across different tools and different frameworks that you can find to be able to leverage. And we're going to cover a hands-on use case of how to apply all of these things. And as I mentioned, if you have questions as the talk goes on, please ask them on the chat. I'm going to be stopping and addressing questions as I go to each of the sections of the presentation. So in regards to the use case, we're going to be doing real-time processing on Reddit dataset. So Reddit, as we all know, is called itself the front page of the internet. So what we have is a dataset of Reddit comments from our science that have been removed by moderators, and we're going to be building a model using this dataset. And we're going to be aiming to fix the front page of the internet, because we can all agree that the internet is not full of just positive stuff, but at the same time, we also want to dive into some interesting things that we can see on the exploratory data analysis of this dataset. So of course, we're not going to have time to do all of that, but after the talk, you can actually dive into some of the data exploration on the Jupyter notebooks. So before diving into streaming, we need to take a trip to the past. And really, this is not even a trip to the past, because this is the present, right? And the present is ETL. What is ETL? ETL is, as you can see in this architectural diagram, the concept of extract, transform, load. That basically means you take a dataset, you do something with it, and then you put it back somewhere else. And this is how the data processing world has worked since the Stone Age, right? Ultimately, it's not about real-time events. It's more about you take a batch, you do something, you put it somewhere else. The reality is that the world still functions this way. But at the same time, we're moving more towards this real-time thing. But I want to dive into this concept of ETL, because it's still so prevalent. And the complexity doesn't only come with this ETL, three-letter acronym, is that you only have ETL, right? You also have ELT, you have EL, you have LT. And you have tons of different types of combinations of this stuff, right? And the problem is that they're not all just terms that people throw away just because they feel like it. Ultimately, although sometimes that is the case, we all know. But the reality is that these terms actually do refer to patterns of data processing that are present in industry. And this separation is still important. And there are a large range of different tools that are available at your disposal for you to be able to leverage each of these different things, ETL, ELT, ELT, et cetera.  But a lot of these different tools, they are very good, very specifically for certain areas. And they're best when facing specific challenges, right? And more specifically, some of the tools that fall into this, in the context of extract and load, instead of transform, is things like plume or nifi, a patch nifi. Plume basically allows you to move data from different databases. Ultimately, nifi, as you will see with other tools, it tends to be very ambiguous of the division. Because, for example, with nifi, they also leverage a lot of the transform step. So although it is used a lot to move data around, it also can be used for actually transforming it. Now in the extract load transform, that would be more on the data warehousing perspective, right? You load your data to something like elastic search or some data warehouse and once data is loaded, that's when you actually transform it, right? And then there's others like ETL, which is the sort of more common extract transform load, which encompass all of the different areas. And these are tools like Uzi or Airflow, if you have heard of that. But today, we're going to be using a good old ETL step, where the transform step is a human with a Jupiter node, right? So that's going to be, in our case, our non-real-time process. And what we're going to be doing in the context of ETL is going to be the training of our model. And the reason why I'm talking about ETL and Batch is as a step towards this concept of streaming. And the reason why is because Batch and streaming ultimately get brought up in the context of, is it one or the other? When should you bring it? When should one be better than the other? But in reality, what we're starting to see is that, of course, you have some tools that specialize into the streaming world and you're going to want to be leveraging them in the context of eventing or real-time processing. But there is a larger and larger intersection of both platforms, sorry, of both concepts and even more of an overlap that tools try to unify those. And more specifically, you often find yourself where you actually need both. And today, for example, we're going to be training one model, which is not going to be real-time, but then we're going to be actually putting that model in production for stream processing, right? And it's ultimately going back to that massive ecosystem that we were talking about. It's not about just running with a hammer, trying to find a nail, but it's instead trying to understand what is the right tool for the challenge, right? And then as I was saying, right now there's this massive trend within the streaming world to unify both worlds, Batch and streaming, right? The streaming community is trying to bring in this concept that when it comes to stream processing, if you take a batch of data, it would be the exact same as if you were just streaming that. So if you take a file and you pass it through to a stream processor, that would be still in the same sort of framework that could be used. And at the same time, when you take a real-time stream, let's say for example, events that are coming out of a system, you may want to also take batches that would be used for processing. And tools like Spark actually do this, where they actually use this concept of micro-batching in their stream processing, where as you're aware, I'm still going to go into detail in case you're not as familiar, Apache, that technology, what it basically does is it allows you to actually process large-scale sort of data sets, right? Apache Spark is able to distribute horizontally that massive data set that may not fit in memory and then process it and get some insight. However, when it comes to Spark streaming, what it basically does is it actually takes chunks of the stream and then sends it to Spark so that it actually processes it in micro-batch. And the stream community, they basically say, this is not real streaming, right? It's fake streaming, that doesn't count. I do have to say that in the most recent versions of Spark streaming, they actually introduced the ability to be able to process in actually every single data point as opposed to micro-batches, right? So that's just one thing to mention. But yeah, what is interesting here is that there is a lot of push towards this convergence of worlds. And we're going to cover this into a little bit more detail in a second. But before diving into that, I want to dive into some of the concepts that are specific into streaming. When you get a stream of data and you actually are processing that data, you have new concepts that, you know, are quite common in that community but are important to gain as foundation so that when you approach a challenge, you can use those best practices. The first one you have, you know, most probably already come across that is the concept of Windows. This is basically the ability to take on a specific window of data points and be able to actually perform a computation that could be a stateful computation across that specific batch, right? So in this case, you can have tumbling windows which is basically non-overlapping windows or you can have sliding windows where basically you actually do overlap on the windows that you gather. And this is where the point about the unifying worlds come in which is basically saying, well, you know, you are dealing with a stream or you can actually convert that streaming problem when it's necessary, of course not always into this sort of like micro batch problem specifically when you want to actually gather, perhaps let's say an average across every second, right? Or something like that. And one of the things that we're gonna cover in two slides is specifically the concept of, you know, the time of the events themselves, right? So you may want to have the actual sliding windows or tumbling windows on the time that they arrive but perhaps you may also want to process, to make these windows on the original event time, right? Because remember that, like the time that an event arrives is not the same that the time the event was actually generated, right? So perhaps your windows are one second, not on the time that they arrive but one second on the actual event time, right? So, and this is something important, bear that in mind because we're gonna actually cover that in a couple of slides and that's gonna be an important detail. The second point is the concept of checkpoints and checkpoints are basically like how you have it in a game, right?  So in this case, that's the same intuitive concept, not the same, but it's an intuitive way to understand that concept, which is basically that you're able to keep track of consumers basically where they were able to actually read last, right? And this is an important concept when it comes to streaming because when you actually have a consumer, you can also have perhaps a consumer group, right? So you may have multiple different consumers actually reading from a queue and you may want that the checkpoint is for the entire group. Now, checkpoints, the other important thing is to understand when you're actually marking that checkpoint. Are you marking that checkpoint on the moment that you read it? Or are you marking that checkpoint on the moment that you're actually processing it, right? Because ultimately when it comes to the commitments that you can get when it comes to stream processing, you can actually have things like at most one delivery, at least one delivery, at least once delivery and exactly once delivery. And as people that have actually worked with streaming processing before, the exactly once is the hardest and it's a really hard challenge that it has to be reassessed and it has to be really thought well when implemented. There are some data streaming processing frameworks that offer it, of course with some constraints and assumptions that need to be in place, like Apache Kafka that allows you to define these things at least once, at most once, et cetera, et cetera. Of course, as you approach your use case, it's important to bear in mind which one to use. And now the key other point to cover is the concept of watermarking, right? So we talked about processing time versus event time before. And watermarking basically says if you're perhaps doing a window computation, say perhaps you want to have the average of some attribute on every second, right? Your watermarking is basically saying how far are you going to be able to wait in regards to holding a buffer for you to be able to count events that arrive late, right? So you're processing on windows, not on processing time, but on event time. If an event that was supposed to come five minutes ago arrives right now, if your watermark is still seven minutes or anything above those five minutes, then you'll still be able to take that and then bring it to that window, recompute the window and have that to be able to use as your window processing. So the concept of watermarking is an important one, primarily as it allows for the ability to consider events that come later. And again, this is a component that has to be taken into consideration when building your stream processor, et cetera, et cetera. And some frameworks provided out of a box, some others, you know, you have to like build it. Some of the tools that are available at your disposal for stream processing include things like Flink, Kafka Streams, Spark Streaming, Fost, Python, and Seldom, which we're going to be using in this specific context. But before that, before we actually talk about the processing, we talk about what is the thing that is being processed. And for that, we're going to be talking about the concept of the machine learning workflow. And this basically consists of two basic streams. The first one, which revolves around the training of a model. Basically, you get some data that you want to use for a model to learn from and you convert it into features that the model can actually need. And then you are able to iteratively train the model until you're happy with that specific model. Once you're happy with the current accuracy or performance metrics, that's where you can deploy the model and the new unseen data can be processed by that model that you have been trained. Specifically in our use case, we're going to be using the two sets of Psychic Learn and Spacey. And what we're going to be doing is taking data set, which is basically Reddit comments and predict whether this would have been moderated by the admins of the page. So what is going to be happening for our model to be able to predict and learn from this data, we're going to have first the text as it arrives. We're going to be using a text cleaning component, a tokenizing component, we're going to see what it means, which is using Spacey, a vectorizer component and then a logistic regression machine learning model. What does that look like? Well, let's see in more practical terms. If we have an input data that says, you are a dummy, what happens is that goes through our text cleaning, as you can see, the first stage removes the symbols, then it goes to a tokenizer, which is basically using Spacey. This converts the string into actual stream tokens, which in this case it would be pronoun is dumb. And then from that perspective, you pass it through a vectorizer, which basically converts this component into vectors that then the model can learn. And then you can pass it to the model so that it can train that specific model, which in this case it would be predicting whether it's true, it should have been moderated or false, it should not have been moderated. So this is basically the pipeline that we have trained. If you're interested on more about this data set and the model, we actually have a Jupyter notebook that delves into the exploratory data analysis. And this actually has a lot of really interesting insights of the data set that we used. We can delve into that for a little bit more detail. Now we have a model that we've trained, there's the question of how do we go from either the model weights or the code that we just created into a fully fledged microservice that has a RESTful API or a GRPC API or in this context, a Kafka API, a Kafka interface, producing metrics, producing logs with all of the components that a microservice would require. And this is what we're gonna be using seldom core, which is this framework that allows us to convert a piece of code or artifact into a fully fledged microservice. And basically the general steps that are involved to actually containerize code is to first encapsulate it with a Python class that has basically an initialization where it loads the actual code or the actual model and then a predict function, which is where we actually call our model. All of the input requests that you send are basically the ones that are passed to this function and the whatever is returned is basically on that microservice API. So ultimately we're able to use the Sylvan utilities to convert this wrapper into a actual Docker image that can then be deployed into the Kubernetes cluster that then can receive REST requests or in this case, the actual Kafka streaming topics to process from. And if you're curious, you can actually see in the link below is a full example of how to containerize this model which is basically a Jupyter notebook that will guide you to do that. The way that we actually build a wrapper, if you remember, we actually trained a couple of models in this wrapper in the initializing method. It actually just loads the pickle or in this case, deal. It loads the trained model that we actually created and in the predict function, whenever you send a request or whenever a new data point is added into the Kafka topic, then this actually passes all the way through each step of that pipeline. So the text cleaner, the tokenizer, the vectorizer and the model to actually return a prediction. So this is basically building upon all of the different concepts that we touched upon earlier in the slides. And now, okay, we have trained our model. We have containerized our model. How do we deploy it? So now in this case, we're gonna be looking at what the actual architecture is. And what we're gonna be having is we're gonna be using the Kafka queue with an input topic and an output topic. And as we deploy our Seldom model, this Seldom model will be able to consume using the Kafka native interface, will be able to consume from the input topic and produce the output into the topic itself. And again, you can find the third example and last example of how you can actually use the Kafka interface in this notebook, but what we're gonna be seeing here is how do we actually deploy this? And with Kubernetes, as you may already be familiar, you deal with the actual YAML definitions. In this case, Seldom provides you with a YAML, in this case, it's a custom resource. Here we define the name of our deployment, which is called Reddit Kafka. We actually defined that it's using a Kafka protocol. And then we actually specify that it's using the NLP model, that the NLP image that we built in the previous slide with the tool sets. And ultimately, we specify how it can talk to the Kafka cluster using the input topic, the output topic and the brokers. And then finally, you can actually specify that this model is the one that is being used. With Seldom, you can actually define complex graphs with multiple nodes, et cetera, et cetera. But in this case, we're only using a single node deployment. Once we actually provide that, we're able to then produce some data, in this case, using the Kafka console producer, which then as we pass some input text, it would then be going into the input topic. It would go through the actual Seldom model and then it would be produced to the output topic, which in this case, we would be able to visualize using the Kafka console consumer. So in this case, what we're able to actually get is all of the processed data points that have gone through the model. And even though right now, we actually covered a broad range of different components, all of this is available for you to actually try out in different steps, right? So if you're more interested more in the machine learning side, you're able to delve further into that as opposed to if you're more interested in the deployment side, which it's all covered. Now, the key thing about this is at scale, right? Being able to scale this architecture horizontally and vertically. As you know, Kafka can actually scale in regards to number of brokers that allows for massive throughputs. And then similarly with Seldom, it allows for horizontal scalability, which means that you can have multiple replicas that have a consumer group that then all are actually ingesting from the input topic and then ensuring that if one of the broker dies, then you have the different brokers. If one of the actual microservices dies, then you have the multiple different brokers with the consumer group. So again, this is very much a high level overview of the intuition, but you can delve into some of the examples that are provided in all of the links below. And just as a reminder, the slides are available on this link, bit.ly slash Seldom Kafka. And these are the slides that will also contain the links for you to be able to access all of the examples. Now, with that said today, we covered a conceptual introduction to stream processing. We delved into the machine learning model and the training. We covered some of the tools available and then we delved into both the containerization and the deployment of the Kafka pipeline. And with that said, thank you very much for everybody that joined this talk. And I look forward to taking questions. If anything, you can feel free to contact or reach out as I would be more than happy to provide any insights. And as I mentioned, all of these examples are open source together with the projects, the underlying projects. So if you think of any potential improvements or anything that is in your mind, feel free to open an issue on the respective GitHub repositories. And again, thank you very much. And yeah, looking forward to delved further into the discussion.