Hi guys, welcome to a new vlog doing a few chores with Valerie over here. I'm gonna go out in the scary world. The world is scary right now. Do you have any positives tips to give to people? Yes, I'm going to start posting on my universe responding Facebook page and I'm going to periodically post affirmations to help us be positive during this time period. You have to forgive me, I'm a little whores, I've been teaching a lot, but... I'll link it down below. She also might be starting a YouTube channel. Yes, that's right. Well, I have a YouTube channel, but it's not very much right now. But we're going to go out do like... And podcasts. I'm going to start doing podcasts too. She has the Yeti microphone, but we're going to go out do our couple of chores that we do. We got our gloves, and I think we're going to go get food for dinner. Oh gosh, these break. If anyone's wondering what that noise is, that's Valerie's breaks. I swear though, the car's okay. Anyway, we're just going to the grocery store basically and... Fresh market. Oh, vitamin store, yes. So we've been taking vitamin C and we're going to go to Fresh Market to get our chicken and then... I'm going to get zinc too, a friend of mine told me that zinc was good for the lungs, and then I should take 30 milligrams a day, but with my multiple vitamins I only get 15, so I have to find zinc and it's 15 milligrams. And we're going to go to the grocery store to see if there's probably like paper products and stuff. So come along with us. First stop of our journey, the vitamin shop, sorry for the overexposure. Made it into the vitamin store and we're looking for zinc and vitamin C. It looks like they have everything you could possibly want when it comes to vitamins. This whole store is filled of nutrients. So their vitamin C section, everything is out. There's vitamin E. Oh my gosh, Valerie. There's no zinc. So I don't know what we're going to do here. All right, so we got some good supplement. They didn't have exactly what we were looking for, but we got better choices. So this one is called Cors Decepts. I don't know. You know what it's called? But this is a good replacement to help lung functions. So it'll help you breathe better. Then the next thing we picked up is a bio-ustin and this is a good supplement for vitamin C. It's actually a lot stronger and he was telling us a lot of the good facts about it. So highly recommend that. If you have a vitamin store near you, check out those. We got two of each. All right, our next stop is the Fresh Markets. We're going to see if they have some chicken and stuff. And Valerie likes the sign right in the front of the store, which I'll show you guys. So this is a sign Valerie likes. For anyone not feeling well, please do not enter the store. Also very responsible sign. Oh wow, they marked it. So you know like what six feet is. Here's the meats we got here. I'm going to get the last three legs that are like right there. And then Valerie and she are going to have thighs, I think. You're having thighs? Yep. The most important thing on the list is Valerie's cookie crisp. Where are they? I think they're over here. Yep. Here are the Beloved, the other Beloveds, not to be confused with Beloved. The backpack. The backpack. We'll have to show that. We'll have to show her on the vlog. Valerie has this backpack called Beloved. I gave her a backpack and I'm like, it's kind of an inside joke. But we'll eventually show Beloved the backpack on the vlog. Right? Right. So we are also getting some sour cream. We have our meat, let's look for soap, we have some cookies. Next on our list is soap, I guess. Scratch the soap idea because we're going to just check Publix, right Valerie? Yeah, they didn't have any antibacterial soap here and I prefer that right now. Yeah. I do have a P.O. box. These letters are not for me, they're probably for Valerie's mom. But just want to say, if you guys ever want to send me anything, like all the info is in the description box. Okay, so now we have made it into a grocery store after getting our mail delivery. Thank you for getting that. I did get them in a secure life, water. Cool. I think I might as well pick up a case every time I come to the store. I agree. But because when they get their shipments in. And we're also getting more lemonade because lemonade is so good and also Sherry wants to mix some with some iced tea, so we're going to get some more of that. There is no lemonade except for the small ones, we've got some potatoes, Valerie's cooking dinner tonight. So hype. Okay, so there's all those juices over there, so I actually ended up getting a bigger size of lemonade, yeah I'm going to take these back. But you know, everyone loves the Publix brand because Publix brand is so good. Can we put both of these in your refrigerator for now? Yeah, well I think they'll fit. Valerie's adding some Diet Pepsi to the haul here.  I'm actually kind of just doing the haul here because I can't really do the haul because Sherry's working and she's on the phone so clearly we have to be quiet and everything. Got some water, I think I already showed that. I already showed the two drinks, so we got an onion for tonight I think. And we got some potatoes. She's cooking tomorrow.  I'm keep on getting my days mixed up. Wait, what are we having tomorrow? What's tomorrow? Chicken, tomorrow's hot dogs. Oh, hot dogs, yes. I did tell you guys that we're having a hot dog and chip and dip night, so it's going to be fun. We also picked up some pop secret snack size butter because I love to eat those as snacks. They're like a hundred calories and then we have my oreos, you know my love for those. Gonna get some turkey here. This is the honey smoked turkey breast. So good, at least they have this because for a while like the meat stuff was all sold out. We also added some of our Adkins shakes and the pretzels were on sale like two for $6. These things are, I mentioned this in my other video, they're massively expensive which is not cool Adkins, but it's okay. Oh my gosh, look, so Corona beer is on sale at our store because people think you drink this, you'll get Corona virus. That's so funny. Okay, so we are on our way home and I'm just vlogging because I think Sherry's going to be off pretty soon, but I'm going to obviously show you the wonderful dinner Valerie cooks. I want to say this is the first time that I'm not going to work out on one of my exercise days. It's a big deal guys, it's a big, big deal. So it's a big deal that I'm not working out, but Valerie, just take the day off. I'm taking the day off, I mean we need a day off. I've been going at these workouts now consistently on the same days for 10 weeks guys, so I'm going to take a rest rest day and I'm going to get back into the workouts tomorrow. You think that's okay Valerie? Yes. Oh, and this is being recorded on Saturday, so just to give you a perspective, like Saturday, Saturday, Saturday the 28th. God, this month's move is so slow. I know, I know.  It's unbelievably slow. There's nothing like a worldwide pandemic to make time slow down. Sherry hasn't been in the vlog yet. I have not, hi. So tell us how's work been? Work's been kind of stressful, people aren't always nice on the phone. This is a PSA, be kind to those that are still like working in service jobs, you know.  So today's pretty nice, but be kind. Occasionally you get somebody who just can't be, you know, can't be pleased and I totally understand or feel like they're using money and I know a lot of people have planned for a long time to take a vacation and they don't. So it's hard on everyone. On another note, Sherry's helping cooking. I am. I'm helping with the cooking. I'm helping turning the potatoes. Turning the potatoes. I've done that job. I know. We have some green beans and Valerie's outside cooking on the grill. There's Chef Valerie. Thanks to the cook.  All right. So we have chicken. The mothers have some thighs. We have some drums. We have our green beans. Mother's green beans. We have green beans with bacon. Yeah. And then I'm here. I don't have. I don't have. Valerie's potatoes. Valerie's potatoes. Valerie makes special. The next day. All right. So it's the next day. Is this in my hand? What I'm doing it? Um, it can be.  Anyway, my mom's learning a tech talk. She was going to clean something anyway. We're doing this one with the squirt bottle here. I have more views than I do. I get like five. Okay. I'm going to count how many she's doing. Okay. It's going to be much easier. It's that second part. Yeah. So funny. Duh, duh, duh, duh. Okay. It's only one in between. Yeah. Sherry, how's your TikTok making coming?  Oh, maybe. Not sponsored people. Not sponsored. All right. We're going to try it again. Okay. Okay. Well, it's Eat-a-thon. What was the name of it? I wrote it down. Snack-a-thon. Snack-a-thon tonight. I almost forgot to vlog. The mothers are watching Mandalorian. I'm listening to YouTube. But we have some hot dogs here. I had the y'all rake. The mothers had their green bean casserole. Wait, some chips? That's not chips. That's dip. Here's the chips.  So sometimes you just got to live a little and we're in isolation right now. So we're going to eat. Well, they're already eating. And for dessert, I'm having a Diet Coke float. This is the Publix brand ice cream, the best brand out there. I'm impressed with the like name brand stuff. Love the Publix brand. The next day. Happy Monday. Happy Monday. It is currently 8.30 and Sherry and I tell them what we're doing, Sherry. We are going to Krispy Kreme donuts. I've been craving donuts, but I really prefer Dunkin' Donuts. But the other two members of our household really prefer Krispy Kreme. It's Krisp... I just woke up, guys. Sorry, I can't talk. It's Krispy Kreme Hands Down. Leave a comment down below. Are you Krispy Kreme or are you Dunkin' Donuts? Sherry's all for the cake donut. I am. But I'm totally not like the magicalness of the sugar just melting in your mouth. It's just beautiful. They're good, but I like something a little more sturdy, a little more dense than that. That's not a real donut. I will eat Krispy Kreme and they are, I have to say, they are like liquid goodness when they're warm. But... And they have a drive-through. I haven't been contemplating on getting a bike. Really? Yeah. But then I would have no one to ride a bike with. I have a bike. I have to get my bike fixed, but... Exactly. But all these people riding bikes and stuff, I'm a little jealous. I haven't had a bike since I was eight or ten. I sold it to get an iPod Touch, don't judge. But all the cool kids have iPod Touches. So I had to get one somehow. So I was like pawning off like everything I could get rid of. So I got rid of the bike and my neighbor bought it for 20 bucks. But anyway, I just wanted to vlog this because it's an outing. She's eager. She wants her donuts. She's yelling at the person for not going on the green light. Yeah, I guess today and yesterday were pretty much pick-out days. We had some bad foods, which I'm probably going to have a little bit more bad foods maybe later in the week. But I'm kind of like done for now because when you eat so clean, it's a lot different when you're like stuffing your body with like bad foods. But it's if you know what I mean. I'm sorry if these vlogs aren't that entertaining, but honestly I was telling Sherry, I feel like my life hasn't changed that much except for like going to Disney in Disney Springs and that sort of thing. But other than that, like I'm working at home, but that's not that big of a deal to me. Because I used to work at home when I did freelance for people and stuff. There she is. There she is. Krispy Kreme. It's open. It's drive-thru. Yeah, online or drive-thru only. That's what the sign says. Two of the traditional cake. We got the goods. So in this box, we have chocolate. So two chocolate. I think Sherry's going to have one of those. Then we got cake donuts and then these are cream filled for Valerie. Oh my gosh guys, look at heaven right here. These are the typical boys. Oh my gosh, I know that's sad. But we could have them like later. Yeah. I'm going to take one. You got to have it when it's hot. So magical man. This is it for this video. Give it a thumbs up. Don't forget to subscribe. And I'll see you guys in my next video. Bye.


Today, I had the pleasure speaking with Frederick DeGray from H2O Innovation. How are you today? I'm great. You're self, Patricia. Of course, just before we started this interview, we were discussing, you're not a tech company. You're actually a clean tech company. You're the kind of company that ESG investors who care about our planet should be buying. And didn't you just tell me you just had your best results to date? Exactly. I mean, we have published last week, you know, the best quarter we have ever announced in terms of financial performance, both on the EBITDA improvement margins, on the growth, on reduction of our balance, of our debt on our balance sheet, so everything is moving in the right directions, frankly.  That was our conclusion. Are we right, Frederick? Well, we will certainly benefit from that. I mean, it's a little bit too early to see the impact, you know, the next month, because it will take a number of years and months to see this money being deployed and start to hit us. But certainly, certainly, there will be projects. I mean, we're already seeing, you know, how the shift is currently happening in the southern U.S., where there is a lot of new projects for water reuse. I mean, they had a little bit of snow this winter, so more and more they're facing, you know, challenges related to water shortage, you know, water scarcity. And it's just a growing concern. So investment will remain and we're very well positioned to capture that. Well, speaking of well positioned, you're also been always a very professional and a great interview. So I'm just going to run through some questions for you because you are always putting out milestones and news. So let's start with the two new projects and renews for operations and maintenance contracts you just put out this last week. Can you talk to us about that? Yeah, this is this is quite nice, very, very happy. I mean, first, we want a new project in Texas just around Houston. So we're adding to the existing mud clients that we have, so Municipal Utility District. So this is another one that we're adding down there. On top of that, we're extremely happy to see this new project just outside of Calgary being secured for a food and bev industry customer. So it's a large industrial customers that will be operating this plant. So very, very happy of this win because it's right up our alley. It's an MBR plan that we're going to run and operate for them. So this is a fantastic. And we have seen over three years or so a tremendous evolution in our sales around, you know, Calgary area and in Alberta. So this is a big milestone for us. Well, congratulations on that. And then further to that news, you announced earlier in the month, you're increasing your presence in Latin America and your North American with your focus on water reuse. So tell us about the Latin America and then jump right into your water reuse because Matt Boulson was writing about that today for us. And he was he was drawing investors' attention to this prequel aspect of H2O for revenue. Well, for us, I mean, Latin America, we're starting to put, you know, the basis of our business platform there. We completed an acquisition back in February 1st, the acquisition of GMP, which came along with also a branch in Chile in Santiago. So this is giving us the opportunity now to have employees of H2O working down there, covering the space on top of having a number of distributors covering the entire Latin American region. So we have now a total of about 20, 25 distributors covering the entire space going after mining and digital customers, municipal customers. And I think, you know, the best is yet to come in Latin America in terms of growth, in terms of water infrastructure, in terms of needs. So so we want to be there. We want to be able to participate to the upcoming growth. We just hired a new VP, Alejandro Ostrignolo, who's joining us also. We will put some in faces and help us out to better coordinate this effort towards Latin America. And of course, you've already got, what do you have? Three analysts with my recommendations on you presently? I'm sorry, we have six, Gracie, now. OK, so we have record revenues. We have technology. We have patents here. You're doing the number one interest we should all have on this planet, which of course is hotter. I have to ask you, if you were talking to an investor right now, what would you say would be the most compelling reasons why H2O should be in their portfolio today? And I'm guessing the stock is never going to be a better buy than today.  And we realize and we show we have shown how we can navigate through this crisis. We have proved that not only our business model is the right one, but that the water space is key, right? I mean, people will continue to drink water. They will continue to flush their toilets no matter what. And this is what we're providing. So among all the different sectors, we're not related to a currency, we're not related to a commodity. We're just part of a growing concerns about water quality, water resource, water quantity. And this is what we're doing. We're just, you know, building and building these new infrastructures and servicing these new infrastructures. So all this is starting to significantly impact in a good way our balance sheet and our PLN, making ourself, you know, execute quarter after quarter and just improve our numbers. So I leave it up then to the investors to take their decision. But if they want to invest in the right space, water is the right space to invest in H2O is definitely a company that stand out in terms of executing our business plan. Well, I'll tell you, thank you so much for joining us today. And after that finale, I'm going to set you up with critical materials expert Jack Lipton for your next interview. Thank you so much for joining us and everybody. If you don't have H2O innovation on your watch list, we recommend you. You put it there now. Thank you. Thank you, Tracy.


Hi everyone, Joe for jaspyscasebreaks.com with another Donner's football hangar box with six of those playoff football spots being given away And it's in that one box playoff NFL break where you can win a Super Bowl squares and a $6,000 130 box Super Bowl mixer spot $6,000 value crazy But first before we get there we got to do this big. Thanks to this group for making this happen There's all the teams right there and then we'll do the six-spot giveaway the different dice will at the end of This video So for names and teams though two and a six eight times one two three four five six and eighth and final time eight times for the teams two Three and eighth and final time. All right. Here's the first part of the list right here and The second half of the list right over here. No trade windows in these things And there they are Let's just alphabetize print and rip. All right. Once again big thanks to this group here Here's the official list hot off the presses See what we got in here. Well, I like this card on the bottom already Here's Jamar Chase the rookies Going to Cincinnati Michael Robinson this guy have a shot at Super Bowl MVP maybe and usually it's a it's a quarterback award And I think it's been a while since a running back has won it But why receivers do often get some consideration? Jalen. Oh, there's another good receiver right there, too That's for Miami. That'll be for Joel and what if Vodell Beckham, Jr. One Super Bowl MVP Here's Trey Lance Elite series rookies going to the Niners see birch There you go. All right, so not too bad of a of a hanger box Now let's see here's some big hits here, too, let's see who's gonna win those playoff box spots So let's roll it and randomize that list Five into two lucky seven times. So it's top six after seven good luck one two three four five six and Sevens and final time so top six after seven so from seven on down. It's gonna be sad times Very close Joel But hopefully your name's still up there. All right, but hey happy times Hey, hey, happy times for mark is happy. So is Jason. There's Joel Michael Joel again and Patrick last spot mojo Nice, so we'll see you for that playoff break a little bit later tonight. Just piece case race calm. Bye


No other ruins anywhere on our planet is surrounded with more controversy than that of the great pyramids of Egypt, or indeed its accompanying plateau. There are many factors to consider when it comes to Egyptology. Within academic fields, there are many no-go areas of study. Although hard work and research within permitted areas has taught us a great deal about the previous 4,000 years of the site's inhabitants, yet regardless of the most astute academic thesis, there remains three proverbially large elephants in the room. When it comes to a full or even a mere fraction of an explanation in regards to the origin of these seemingly impossibly huge pyramids remains patiently absent. No accounts, illustrations of any kind from the era exists. It is simply illogical, especially when one considers the sheer feet these structures must have been. We have presented many previous features, polygonal masonry being present on the pyramids. Eroded yet younger casing stones protecting inner megaliths, clearly of a tremendous age. Salt sediment found encrusting the lower chambers, and so on, suggesting not only that the pyramids are much older than currently claimed, but were pre-flood ruins. Thus, questions arise. Just how old are the great pyramids? In addition to our study of the pyramids, we have also in the past asserted that the Sphinx was originally a lion, which interestingly correlates to the following hypothesis with fascinating accuracy. The Orion theory. The coincidence with pyramids aligned with Orion's belt and other significant constellation positions. Baval and Hancock support the theory, believing the great Sphinx was begun in 10,500 BC, creating reference to the constellation of Leo and the orientation of the entire complex with the Nile River and even Milky Way, claimed by them as connected respectively. Zeptepe, using similar methodology, put the age at over 13,000 years. These are clearly astonishing proposals, but the current paradigm for their chronology, we feel, is far too short a time span, and due to our own research, which has uncovered evidence indicative of pre-flood origins, copper tools for such an accomplishment a mere insult to intelligence. Yet, thankfully, due to these various takes on events, their age remains highly contested, and to us, a mystery which is incredibly compelling. The Queen's Chamber, which lays within the great pyramid of Khufu, more commonly known as Chiops, has astonished, shocked, and mystified Egyptologists since its mysterious existence was discovered. The intrigue into this elusive chamber, along with its mysterious adjacent shafts, comes as no surprise once one understands the anomalous characteristics of their construction. As we have already covered before, massive cover-ups have been suspected as taking place surrounding this mysterious chamber since its discovery. Strange shaft tunnels, said at a 45-degree incline, no larger than 20 cm in diameter, run away from this room, and no one seems to know why. Not only would these ancient shafts require being hermetically sealed during the pyramid's construction to stop them from becoming blocked, but the excruciating effort that would have gone into making them becomes all the more of a confusing undertaking. Since you realize they were not even connected to the chamber, but hidden 40 cm away from entering the tomb within the walls, completely invisible from the inside of the burial room located deep within the structure. Chiops, noticeably being the only pyramid to have ever been constructed with such shafts, making their addition a popular mystery within Egyptian history. One leads out from the subterranean chamber, two lead out from a termination point some 40 cm from the wall of the so-called Queen's Chamber, or now popularly suspected to be that of an alien tomb among ancient alien specialists, and two from the King's Chamber above. Here is where our story becomes interesting. Rudolph Gantenbrick, famous for actually discovering the blocking door within one of the Queen's Chamber shafts, which could lead to an as-yet undisclosed tomb, has also made other curious discoveries within the Great Pyramid. Discoveries which could only be explained by modern covert explorations of tunnels that were supposedly to that point unexplored. Gantenbrick's cache being but one example of these mysterious finds, deep within the tunnel systems in the royal chamber, at a 90-degree turn going vertically upwards.  Also, during initial location attempts to find access tunnels leading to the Queen's Chamber, several blocking stones required removal. After the removal of the seventh block, a modern era hexagonal steel rods were discovered discarded upon the tunnel's floor. Each section of the hexagonal steel rods measures 2.7 meters in length and was fitted with a round socket which allowed them to be joined to the next section. In one of the lower shafts in 1872, Wainman-Dixon found three more objects, which could be considered proof of prior covert exploration of the mysterious northern shafts. A copper grappling hook, about 5 centimeters in length, accompanied by a small grey-green stone ball and a broken-off piece of a square wooden slat or rod, about 13 centimeters long. The woodwood today be the most intriguing of his finds. These artifacts, expected to be remnants of the grave robber's tools, could have been carbon-dated, yet this fragment is the only one of the three to now be missing out of the London Museum's collection. Unfortunately, in his writings, Dixon doesn't say in which of the two lower shafts he actually found the objects, but he mentions them in connection with the northern one. Not only did these obviously highly intelligent people leave evidence of how they must have gotten in, but also traces upon the previous untouched ancient walls of the shafts within Cheops, clearly left by their previous robotic technologies. Other square metal rods have been recovered, along with other artifacts discarded within some tunnel systems deep within the ancient structures, meaning these guys got to the treasures way before we did. Interestingly, reported evidence of covert excavations continues to this day. Heavy-duty electrical supplies discreetly running into and trailing deep into the pyramids have been noticed and photographed by some of the more astute tourists. Witnesses to the sounds of heavy machinery being used beneath the site is also frequently reported, yet rarely followed up. It seems it's not a question of whether brilliant minds have achieved the seemingly impossible in penetrating these secret layers, but more a question of how and what astonishing finds have possibly been kept concealed. Since the rediscovery of what is unquestionably the most puzzling, astounding and enigmatic sight on Earth, the great pyramids and the Sphinx of Giza, we have been led to believe that what could be described as an astonishingly accurate, yet somewhat vandalistic later addition was once put there by a caliph named Al-Maman, now popularly known by its coin title Al-Maman's forced entrance. This title, although argued as his work, has a pretty compelling tale attached to its possible original purpose. When one actually looks into what an incredible achievement this tunnel once was, it becomes apparent that it was cleverly bored by an ancient people, far more advanced than a ninth-century caliph. Additionally, possible hypotheses have been put forth as to its origins by individuals who may have known of entrances into the pyramid. We in the modern world have either lost knowledge of or have been prevented from knowing about their existence. These doorways made of stone perfectly counterbalance to allow an average-sized man to open and close them. Doorways along the structure's north face that, when closed, becomes seemingly indistinguishable from its surroundings. Are there still secret entrances along the pyramid's northern side? Quote, the great pyramid, a little way up on one side, has a stone that may be taken out, which being raised up, there is a sloping passage to the foundations. End quote, written by Strabo in Pyramids and Temples of Giza, Flinders Petrie. Yet regardless of these additional, highly compelling investigative leads put forward in addition to an explanation for the tunnel's existence, its remarkable accuracy remains a tough thing for supporters of academia's tale of events to explain. His author Ralph Ellis puts it, quote, the main problem with the classical explanation was that Maman's tunnel is rather too accurate for comfort. It tracks into the pyramid in a direct line for the all-important junction between the descending and ascending passageways. It is often cited that Maman had to turn the tunnel sharp left to discover the original passageways, a fact that Ralph had in the back of his mind when they first visited the great pyramid. But he ambled down the forced tunnel, rather mystified, because the left turn, cited in the literature, could not be found. Having backtracked the tunnel and to try again, that left turn seemed to be no more than a slight widening of the tunnel. In fact, the digging was almost right on target. End quote. For how does one know where one is when deep within the passages of such an incredibly huge ancient structure? Secondly, if instead argued as having been started from without, the same problem has to be solved. For how did one know how to create the initial angle? Although it is now the most used entrance and although it has been drawn upon countless plans, to draw an existing tunnel's precise line of descent is far more easier than to have created said precise angle in the first place. But within the great pyramid is the remaining half of what has often been used to create a compelling possible explanation for this tunnel's original purpose. Known as the sarcophagus of Khufu, an anomalous object found within the pyramid, an artifact we have covered in the past. No one can explain how this giant stone object came to be within the pyramid. It would not have fit through the existing entrance tunnels. However, at some time in the pyramid's life, someone smashed into this stone box, took its past contents, and the sarcophagus lid, an object that would also have not fit round the turns of the existing tunnel system. Yet would have fit through the force tunnel, and due to the vandalistic nature of the tunnel itself, could be argued that this damage to the sarcophagi was inflicted by the same group of individuals who built the tunnel. It is often used to extract the so-called sarcophagus's lid. Is this the real past purpose of the tunnel? And if created by a caliph in the 9th century, how did he tunnel so accurately on target, and additionally, where is this lid now? Was this tunnel like the many different layers of casing stones indicate built by a later, yet also lost civilization, one who flourished far before even the ancient Egyptians? We find the evidence to suggest such highly compelling. Hey guys, Matt from Mystery History here. So today I wanted to share with you a confession. A confession by this man. His name is Dr. Allah Shaheen, and he is one of the world's leading Egyptologists. He is also head of the Cairo University Archaeology Department. He rubs shoulders with top Egyptian government figures and members of the Egyptian state of antiquities. If anyone knows about Egypt's most in-er-kept secrets, it's this guy. In December 2010, he was hosting a conference to a select group of delegates about ancient Egyptian architecture when he let something slip. He admitted to being complicit in the covering up of an astonishing secret. He has since retracted the statement and is now denying he ever made it, however, during the conference a handful of press reporters were present. They all ran the same story, shortly after the conference all quoting the same remark, making it rather difficult to believe that he didn't actually say it. In what I can only imagine was a misjudgment of trust, when questioned regarding the possibility of alien involvement in the construction of ancient Egypt, he not only confirmed this to be the case, but confessed to knowing, and I quote, there's still something inside the pyramid that is not of this world. In a previous video I shared how the tomb of Osiris, once believed to have been a mythical god from Egyptian legend, a belief Egyptian antiquities would like to keep alive, was actually discovered recently. The tomb was found by traversing an access tunnel system just a few inches in diameter. From the moment this impossible access passage was discovered, they quietly knew they had found something amazing. The moment Egyptian authorities finally managed to get a robot with a camera into the tomb, a complete media blackout descended upon Egypt. Walls were constructed around the tomb, and no information regarding the find was shared for several weeks. When the world was eventually allowed near the site, the tomb was found to have been conveniently empty. No explanation as to how grave robbers could have possibly got into the tomb has ever been produced. Was Osiris an alien? Was our previous research bang on the money? In an Egyptian legend, the figure known as Thoth was said to have allowed beings such as Ra and Osiris to exist in our realm. Were ancient Egyptian artists accurate in their depictions of these beings as hybrids? If as Dr. Alashahid states, along with ancient Egyptian literature, that there is indeed something otherworldly under the great pyramid, could it really be a portal? And if it is, why hide it? Regardless of what it is, they are definitely hiding something. What otherworldly thing do you think is hidden under the great pyramid? Let us know in the comments. Thanks for watching guys, until next time, take care.


For anybody that is interested in getting more exposure look for those centralized hubs like universities usually particular institutes maybe of entrepreneurship or not they usually exist at every larger university and see if they have collaborations with other partner universities all over the world because what you want to get is as many eyes looking at your particular invention or idea or or technology as possible such that they can give you feedback that's all you need you need a lot of feedback in order to make sure that this is really the right thing to do


All right, everybody. Welcome back to another OpenShift Commons briefing. We'll be live streaming multiple channels on Twitch, Facebook, and YouTube, and we'll collect your questions from there. Today, we have a new hopefully Commons member, KPMG, joining us to give us a talk on their Ignite platform for... Well, we'll find out what it's all for. It's machine learning, data science, all kinds of good stuff. But we have Kevin Martelli and Hongfei Cao, who I'll let introduce themselves in more depth and talk a little bit about the Ignite platform and how they're running in an OpenShift. And then we'll have a technical demo and live Q&A, as always, at the end of this. So please, Kevin and Hongfei, take it away. Great. Thank you so much, and thanks everyone for taking the time out to go through this presentation. Quickly, before we dive into the details, I want to quickly introduce myself. So my name is Kevin Martelli. I'm a principal software engineer at KPMG, overseeing our big data software engineering team. A lot of stuff related to cloud containerizations and end-to-end deployments with machine learning. With me, I also have a colleague, Hongfei Cao. Hongfei, maybe a quick introduction on yourself. Yes, hello, Ron. My name is Hongfei Cao. I'm a big data engineer director from Red House KPMG. My specialty is on the big data analytics, distributed machine learning deployment as far as cloud computing. Great. Thank you, Hongfei. So, as was mentioned, we're going to give you a little bit of a background around the motivation that we had on building what we call KPMG Ignite, which is our data science platform and ecosystem to bring use cases from POCs into production. We'll drill a little bit into what Ignite is, so the audience can get a feel for what we're dealing with Ignite, how it works on top of OpenShift, and ultimately how it chooses to produce business value. And then finally, we'll go into some architectural components of it. And then I think the last part here on number four, which is the demonstration. And what we hope to do is through all the different parts that we'll take out, we'll walk through a very detailed technical demonstration. The demonstration will be representative of how the business can interact with it, how data scientists can interact with the platform, as well as how engineers can interact with the platform, and how we can productionalize these pipelines on top of OpenShift within containers. So, looking forward to this, and again, any questions, please feel free to type them in as we go. So, the first question that many of you may ask is why do we decide to build and invest so heavily into this platform that we use for our internal data science as well as client initiatives? And probably like many of you could guess, one of the main drivers was just the explosion, if you will, of AI in the marketplace. Many clients looking at it, to leveraging it, how can they use it. And we saw that there's a need there to kind of bring some of these technologies together. And not only from the standpoint of being able to, I would say, productionalize use cases, we do see a lot of people that are able to build cool POCs, but they're not always necessarily able to get them into a productionalized format. So, one of the things within our online platform is how can we come and bring these capabilities from both the POC to realize production, in addition, making sure we have those right hooks. So, how does the business interact with the platform and our scientists and engineers? And again, we'll drill a little bit into that detail. But as we looked at that, there's really about five or so areas that we see that enterprises need to be aggressive in in order to support AI and be able to bring AI more seamlessly into the organization. Some of these areas we do cover by Ignite, and then some of these areas have to be augmented with separate business processes. One of the important things here on number one is around data literacy and building up your data expertise. And we used to think about this more along the lines of something that an engineer or a scientist, a business analyst would do, but this is more holistically now across the organization. How do business folks understand their data? Can they get the right training data? Do they have access to the right data? Within Ignite, you'll learn we have different ways that this could be achieved. We have user interfaces. We have, you know, data sets from the technical side, which we'll talk through a little bit. The next is around technology. And I think we can all kind of agree that there's been a technology explosion in the space. Every day you wake up, there's new technologies available to produce, you know, similar types of outputs. And what technology should you use? What technology shouldn't you use? Where do you want to do your R&D investing around? One of the things we took for Ignite is that we realized that this market was going to be expanding so quickly. So we needed a very open ecosystem. Microservices, containerization, easy to plug in and plug out as these new AI tools and capabilities came to the marketplace. And that's something that we'll show you, too, within this demonstration. Business processes. How do business processes now change? How are people relying on the data from data science? How are people embedding this into their day-to-day, you know, work activities? How are the enterprises adopting this? And then the workforce. It's important that we enhance some of our legacy skill sets. We hire new folks and how does our workforce support this? How do we move from sort of the legacy monolithic into these more agile, you know, quick developing and productionization of machine learning pipelines? And then the final part I'll just briefly touch on is around risk and reputation. I think we all know that there could be a lot of risk associated to some of this. The importance of, you know, understanding your model, understanding the details that are going into your model. How are you managing this? We see a lot of organizations, you know, coming out with ways that they can have explainability and making sure that their models are fixed of bias and how do you fix these things. So there's new risk and reputational risk that organizations are facing in this era. And again, these are some of the capabilities we hope that we can help support through the deployment of our KPMG Ignite platform. So with that, I'll jump down to, you know, what is KPMG Ignite? And we touched a little bit on these topics earlier, but essentially we have the who, the what, and the why. From the who, who is this platform built for? Who were you having in mind that could use it? It was a platform predominantly built for our data science and data engineers so they could build the pipelines. However, you know, without having the business and the ability for others, analysts, et cetera, to be able to put input into the system, it just wasn't necessarily as, I would say, useful. So there's business hook. So whether that comes from annotating and creating trading data, whether that comes from value dating model results, there's different areas where the business and business analysts can come in to work within the platform. The what part. So what is it? And we talked a little bit before. This is a global AI platform. We have a very modular slash micro service type of delivery. So each module and we'll dive into that could be an OCR job. A module can be a model. A module can be a data extraction. So we'll jump into some of these things, what these models are, but they're built in such a way that they're sort of interchangeable. So if there's a new capability in the marketplace that comes out and we want to take advantage of it, you can plug it into the platform seamlessly and as capabilities deprecate, they could be removed from the platform. Then finally, the why. What we noticed is that, you know, as we mentioned before, there was a high demand for these types of capabilities, but more importantly, as organizations sort of took their journey into the AI space and unstructured data and semi-structured, there was a lot of work around unstructured data sets. So loan documents or contract documents, PDF documents, how could organizations get the right information out of them, voice documents, et cetera, and make the correct business decision. So this was really the why of this platform and why it was built sort of with an unstructured data set in mind. Again, going back to sort of the prior point, this was a platform that was built for data scientists as well as data engineers with those business folks. And as we mentioned, it's very important that the business had a way to integrate into the platform to provide the right data sets, both from a training perspective as well as a data validation perspective, back into the process to making sure that we were building the right models, deploying the right models, and kind of meeting the business requirements and expectations. And then the modular component architecture. This is, I would say, the crux of what we built our platform on. We're really building it on the foundation of very small capabilities and services that could be interchanged with other services so you can string together a pipeline to produce some type of output. For example, maybe a pipeline is you need to OCR a PDF document. You then need to break down that PDF document so you can start making business decisions. So maybe you add some, you know, spacey into it to enhance it, or you might do some sectioning of the document, and then ultimately you might make some type of business decision on it. All these different components are modular and kind of executed by themselves, or you can call different components that may reside in different cloud providers, whether that's, you know, GCP, AWS, or Azure. Another area was that it was, you could be deployed via containers, or it supports RESTful services into the platform based on the demand that you need to push through. And then finally, it's around reusability. So all these components are reusable. So if one person creates a component in the community and that component has the capability of doing something, that component gets checked in, can be re-leveraged, and then rebuilt into an image and ultimately brought back into the platform. And again, some of this might be a little confusing through the talk, but once we drill into the demonstration, I think it will become a little bit more real. And then finally, you know, it unlocks the value of unstructured data with surgical precision. So one of the things that we really want to do is be able to get out that content in documents that are able to help the business make decisions. And many times you'll have hundreds of pages of documents, but there's only certain sections or clauses or dates or information that the business needs to be able to make that business decision. And how do you get that information out? And that's all part about this pipeline that can be built on top of the platform about how this, I call it, the documents become mature and more and more enriched so you can start extrapolating out the right information to then be able to make the right business decisions. So as I was mentioning, Ignite is really made up of many different layers as you see here. And what I guess I want to talk about is what I call sort of the foundational pieces that make Ignite Ignite. And we really break it down into these five buckets. So a loom is really the main mechanism of how Ignite communicates from one component to another. And it's a proprietary data type or data element that we use here. But what it does is it allows for one component to receive data in and push data out in a consistent way. And a loom is really nothing more than an enhanced JSON. The next is we have our data science notebooks, which many of you are probably familiar with. Within the data science notebooks, you have the ability to read your own pipelines and workflows to test them, to build them and to deploy them. The data science notebook themselves, it's done through Jupyter Hub and then each user will get sort of their individual notebook. And then we talked about a little bit around how the business and users can get in there. And we have two main mechanisms. We have the annotation UI, which Hanke is going to be able to go through in our demonstration, which I think is pretty powerful, how business users can then annotate documents. And those annotations are ultimately fed in to feed the models. And then there's some management UIs as well. And these are areas where you can build workflows and string things together in a more user-friendly way versus building it out through different code bases. And then finally, one of the things I think which is a gap in some of the other areas that we deal with is around model management. And the ability to manage your model, understanding the statistics associated to your model, many times feeding into your model governance process, as well as serving up models. And I guess one of the things I want to show on this slide is if we sort of understand the platform, I want to walk through from the bottom, from the pools of persistent volumes up through kind of the application layer just to give you a feel for how the platform itself works. So at the bottom, we have persistent volumes for our OpenShift cluster that are attached into the cluster. And if you move one layer, we have our core infrastructure. So what we use is we use Distributed MinIO to facilitate object storage, which gives us better latency, faster read and write times. We also have a Postgres database that stores a lot of the metadata associated to the processing and the workflows. We have our logging and reporting within Kibana and Elasticsearch. We use Kafka as our message broker. But Kafka is really set up in a way that allows you to, why do you execute one component to the next component? Kafka is a way that keeps the queue for the next component to pick up. And then finally, everything's executed across the container organization platform and orchestration through OpenShift. I will take from there. As Kevin mentioned, the whole in-night platform is designed for data science and data engineers to build arbitrary machine learning pipeline and also easily collaborate for different business use cases. So in this architecture, as Kevin covers, there's a volume core infrastructure. On top of the core infrastructure, we have our macro services for each infrastructure component. We deploy it in a secure OpenShift environment and expose it as API for a user to access. And on top of the infrastructure, we have our machine learning pipeline component. You can see we have some pre-built models like Ignite OCR, Test-Write Model, NLP Spacey, Machine Learning, Model Manager, Model Deployer, and Intelligent Domain Engine. So those will be its own macro services, has its own Docker image, and it will deploy it as its own part. Later in one of the demo I'm going to show you later, you can see how we create and customize each machine learning component and execute it using Ignite Platform OpenShift. So moving on to the next page. So here is another view. I got to show you the high-level architecture about Ignite Platform and how we deployed OpenShift. After that, I will show you the actual deployment using our secure OpenShift. So as we mentioned, the whole deployment is container-based and it can plug in different shipwrecks to cloud infrastructure OpenShift or on-prime VM or on-prime private cloud. And we deployed the whole plan from using the CSAD pipeline. So the CSAD panel can help us set up a secret config map and all the infrastructure component deployment including Kafka, the API, the LED search, the AFK, et cetera. And once we have the infrastructure component deployed there, we can use the Drupal Notebook as our data science platform to customize and build any like this arbitrary machine learning pipeline or workflow using the predefined component or Docker image. Here we show once the workflow we built using this Ignite Platform, it starts from the raw PDF scan PDF and we can, you know, to draw in the test write, generate the OCR raw text data, and we can do some sectioning to segregate different text write into each section and run some NLP processing through the Python Space Library. And we have our Ignite customized component called Intelligent Managing to extract certain interest fields, such as contract number, contract borrower name, et cetera. At the end, we have 100 written detection components. The whole model is also, the machine learning model is also deployed and version controlled using MLflow. For those who are not familiar with MLflow, it is an open source project, which offers version control and decentralized storage for any machine learning model. We support the Python Secular model, as well as Spark ML model in packet format, TensorFlow, right-watch model, et cetera. So we are leveraging the MLflow as our model storage, and we have our building component called a model deployer for the deployment. So without any questions so far? All right. I think we can move on to the demo. So for the rest of time, I have three demos for you. The first one is I'm going to show you how we deploy the Ignite platform to a secured OpenShift platform. And then, followed on that, I will show you how to leverage Ignite platform on OpenShift to do the annotation and using MLflow to manage the complete lifecycle of machine learning model. In StarFonds data preparation, how we can use our annotation UI to prepare the training data, testing data to generate the label for our supervised learning model. And all the way to the model prediction classification, and we have interface application tools for you for any user to correct the model output. And as a last demo, I will show you how to use a Jupyter notebook to customize and create an arbitrary machine learning workflow pipeline using the predefined Ignite component. For example, the Spacey, Tesserite, OCR, and IDE. Without further ado, let me jump to the first demo. So as you can see, we are deploying the whole Ignite platform into this secured OpenShift platform. And let me quickly jump to the components we deployed. As I mentioned earlier, we are using the CSED pipeline to help us deploy all the infrastructure component for Ignite platform and also set up services routes for any API or web application exposed to the end user. Also, the CSED pipeline will help us customize and configure service accounts, the map, and make sure the security is placed properly. As you can see, we have several posts, the whole Ignite platform deployed there. So you'll see multiple posts running at this point. And for the data scientist or data engineer, we have the Jupyter notebook for them to test or develop a new machine learning pipeline. You can see each data scientist or engineer and the user, when they log into our Jupyter app notebook, they will create their own pod. So they have a segregated environment for them to test and develop. So they don't need to worry about access control or other users, right? Externally change where we move their code. Okay, so whole Ignite platform. Okay, Hanfei, can you go to IQ first? Sure, it's IQ, yes. All right, so let's quickly jump to the Ignite IQ demo, and then we can come by to the detailed Ignite platform deployment ownership. So here is the tools for data scientists and the data engineer managed machine learning model. The second is we are using MLflow to store and version control in this training model. It actually includes the whole serialized binary of your model. So these tools basically help you selecting the model store. So you can use these tools to check and reveal all the existing models in MLflow. So you can manage and create a new model through this web application. Here I'm going to show you, I already logged in as admin. That can look at it in this Ignite model workspace. We already have three models created. And also let me jump in one model here. So this model is called start date. So what happened is we were processing a bunch of financial services contract documents, and the model will automatically extract the content from the raw PDF to get the start date for this contract. And we don't have rely on any predefined template. This is purely MLP and machine learning model. So the first thing you want to do is you want to create the model using this Ignite IQ admin tool by giving the model name. What's the status of this model? The first status is always the setup, meaning you give the model name, you set up the target accuracy you want to reach for the model. And you start preparing the training and the testing data side. So at that point we are moving to the annotation stage. Once the training data, testing data set is ready, we'll move on to the modeling stage, which will train the model and validate the model using the testing data set. At the end, we have a holdout data set for you to really validate and test your model without the actual true ground truth label. And at this stage, we have the user interface. For any user, they can go to the model result and manually correct or accept the model result. As the last stage is complete. So when we save the model in using this admin tools, what happens is in the backhand, the MLflow, it will create an entry project in the MLflow for this model. Let me quickly show you here. This is the backhand MLflow engine. As I mentioned earlier, this is used as our centralized model storage and model database. It provides version control and also some type of a model serving. But for the actual ignited workflow, we have our own model serving component model deployer. And Hanfei, maybe one thing here is I think a lot of the data sets that are being part of this MLflow are ultimately feeding back into the governance processes that organizations may have. So a lot of these statistics and data sets that are coming out, your confusion matrix, et cetera, can then feed back into the overall governance process of your model management. Yep. Absolutely, Kevin. So as you can see, we have several runs already for the same start date model. This is the training set model. You can see what's accuracy and the number of sample. So here we use this MLflow model to track the model performance and as well as save and manage the actual model binary in the serialized format. For example, for a secondary model, it will save as a Pico format and for a Spark ML model, it will save as a Parquet format. So let me jump back to the Ignite IQ. Okay, so at this point we already created a model using this Ignite IQ element too. Let's move on to the next step, which is we want to start annotate, prepare our data set for the model training. So as mentioned earlier, the whole Ignite IQ tool is designed to manage the complete lab cycle for machine learning starting from the data preparation. So let's see if you have one here. I think we see a lot of struggles around getting the training data and how do you get that trained as a back to your data scientist. And this will allow you sitting on top of the open shift allows you to do to better to get better training data annotating that data. So then ultimately your data scientists and engineers can look at it. So, so good. Yep. Okay. As you can see, we have three Ignite models already loaded in this Ignite IQ. Again, it's back and it's read from MLflow and this model into demo is this a starting model. The first thing is we want to have a set of label data for our model training. And as Kevin mentioned, this is often in the hard right to for the data scientists to get training data if this is a surprise learning model. And for our start data model, we need a bunch of contract data, the raw PDF, and for the SME from business side or data scientists, they can use this Ignite IQ to manually label our target results from each documents. As you can see here, where they have the label for the start date. The actual text is shown here. The reason we can show that actual text is all the documents. The scan PDF is already OCR. So we do have the OCR text without the backhand. So let's say if I draw another bonding box in the different area, you will see that it will return in the text data from OCR. In this way, the SME or data scientists quickly label the documents by just drawing a bonding box around the information they need. And move on to the next document. Maybe just to add a little business context here is what we traditionally see is business users might want to get certain information out of unstructured data set. So for instance, if it's a contract, they might want to pull out the contracting terms or they may want to pull out the effective date or the completion date of the contract. And be able to make business decisions on that. Are they getting the right services for the contract? Is it something that maybe is dealing with live or where you have to figure out what the new rate is to be changed? So traditionally business people, business will ask a lot of questions to documents. Those questions have answers that are in the documents. And the annotation process allows us to annotate those answers, specify them where they are inside the particular delume or the upload from the OCR process. And then allow the data scientists to figure out if they want to use some type of machine learning model to be able to extrapolate out the information or some other technique. To consistently find that right information. So decisions could be made from the business side. That's one of the areas of it. You can also do straight classifiers. If you have a bunch of documents, you want to classify them to different categories. You can label them that way as well. And then the data scientists can see the information to start building their models. Yes, Kevin. Once you finish a manual label for the training data set, we can execute as a model and generate accuracy for the training data. Because normally the data sent here may run like multiple experiments, multiple runs. You can also track the accuracy distribution and the accuracy trend from this history chart. Again, all the metadata performance metric will be stored in the MLflow and in the real-time track here. Moving on to the test, similar to the training data set, we also want to prepare the label data for the test set. Again, data scientists or SME can log into each documents and manually draw the bounding box for the actual label. Once this is complete, they can generate a test data set accuracy for the training model. Once the model is fully tested, we can move on to the last step, which is hold off data set. You can use this data set for validation of your model. Each data set is a new document that the model has never seen before. It creates a new hold off set. This is where we envision having a business SME coming into the start doing the validation of your data sets, of your hold off. How accurate was the actual model itself? Once the SME is prepared as a hold off data set, they can use the test for training model for processing each document. The model will generate the default predictive result with accuracy. At this point, we do see some output by the model looks correct to us, but the result is off. We'll start the menu intervention step to go through each model result and either accept the result or correct it. For example, for this document, the result looks good to me, so I can accept the result. This result is also good to me. Moving on to the next one, since this detection is off, we can reject it and manually correct the right start date. For example, let me pick up this one. That's our effective start date. Let's save this result here. What happened is we just corrected this document result generated by our training model. For this correct information, and for the next training or next model updates, it will automatically use this correct information label to retrain our model to correct the result. Maybe something to mention there, when we initially talked through this, we said that all of our data is stored in a loom. This is the same concept for when you're using these inputs for the business, the annotation UI. When a data scientist gets the output of the corrections that the business is making or the interact or sees that they had in their prediction, they get to see that information into the loom to then retrain their models or update their rules or whatever they're doing to try to extrapolate out that information. So again, one of the things we had data literacy is how do you kind of have the understanding of data through a life cycle is processed. And one of the concepts here, we have our core concepts in our loom, the stored track managing kind of the data through the life cycle of the program or the project, I should say. Yep. Okay. At this stage, we are pretty much done. It was a whole machine learning life cycle, and we can go back to our unit admin tools to update our model status to complete or if the model still needs more holdout testing, we can equip it as a validation step. Yep. And all the whole tools is deployed on top of a unite platform. Yeah. I think you bring up the PowerPoint one quick time before you go back into open shift. I just want to show where you were in that life cycle right there. Yep. If you think about, if you think about what we are doing, there was the activity of the OCR and which was done already that OCR and then feeds into those business input function. So where you can start doing the annotations and where you can start kind of marking up your documents. That then goes back into the data scientists and then once they do and they build and they train the model, there might be a smart sectioning model. For instance, they might want to break the document down in the section so they can more granularly, you know, make predictions on the data sets that are being highlighted. They could add some spacey there to enhance the data to better find the information. But these steps along the path of after you OCR it after the business comes in and gets the annotations and the markups. You know, the next part then is to start breaking that problem down to be able to get the information out that ultimately you want to use to make your business decision. So again, we've kind of run one part of it here to OCR it so you can see the document on the screen like you did. The business will do the annotation you start creating different models, whether it's a model for smart section or you want to reuse this. You enhance your intelligent domain name engine. You add space to get better Richmond across of it. And then finally what on Fay will now show is these components will execute in a workflow that can scale up or down. So if you have to OCR we know OCR is a heavier process maybe it's OCR a thousand documents. You can have a thousand components that are running each time the components complete. It's going back into Kafka to the Kafka know and then your next component in the workflow is picking this up. So we'll do a small demonstration of the annotations been done. The models have been built and now you sort of want to execute this pipeline through these different steps to produce an output. And that's what I'm face going to show you now. Yep. Okay, let me jump back to where we are for the net platform. Okay, so just quickly I'll show you different components we deployed. We have A4 site deployed for most of our storage application including the object storage mail in the distribution mode, two-keeper, HA, Kafka, HA, data search, Postgres, and JFound Art Factory. We also have a bunch of deployment type components shown in here including the WebSys Spark, ML lead component, and a load balancer and janks, HBase API for read write from HBase, annotation tools, component builder, et cetera. So after using the CSAD to deploy the complete in-app platform OpenShift, we can let the data scientist or the engineer to use this platform to launch or create an arbitrary Ignite workflow for their machine learning pipeline. So the first thing. And I guess I'll say just one quick thing. You're going to be using a Jupyter Notebook to show the ability to create a workflow, which is each component that you want to execute and then executing it through. Or as we've seen some other clients and we do it internally, you can call a RESTful service that sends in your workflow that also executes this end-to-end pipeline. Yes, yeah. So this is Python SDK. We created, it's called Ignite Connect. But as Kevin mentioned, you can also directly submit your workflow in the JSON format to launch your machine learning pipeline directly through the Ignite API. Yeah, but the demo I'm going to show you is using Ignite Python SDK to create workflow and execute it using Ignite platform. All right, the first thing is we need to import several libraries, Python libraries for Ignite. And then we will define our workflow. Here, the first thing is we will integrate some PDF, scan PDF image right from the local disk. And then it will come to the workflow definition. Here we will have several components we want to execute in this workflow. The first thing is because PDF is a scanned PDF, we need to convert from the PDF to the image. We are using this PDF tool and we call it page scanner components. You need to specify component name, packet name, docker image with the tag information here. And based on the number of documents you need to process, you can horizontal scale, you can set the number of posts you want to execute for this component in OpenShift. And also how many documents you want to run in one batch. As you can see, this is very flexible and highly customized for your own processing. The next component we want to pick up is Tesseract OCR component. We also need to define some parameters that you want to pass through the Tesseract command. And similar to the first component, you need to define the packet name, component name, image, docker image with the tag information, instance, batch size, etc. We do have two more components, SPACI, which is running the ILP processing and intelligent domain engine to extract interest field right from the financial contract documents. Once you define each component using the Python SDK, the next step is you can create the workflow. So it's actually defined the workflow pipeline by defining a data-directed asset graph, that. Similar to Sparkon has, at this point it's still like a lazy evaluation. You just create the edge for the components, what components you want to execute in your pipeline and with which order. And then it won't execute until you create the workflow and, let's say, kick off the workflow. So I'm going to run this. The infrastructure kind of foundational pieces of the platform exists, but none of these other containers are deployed onto the platform until the execution of the workflow. And then there's the determination of how many do I need, so how many OCR jobs do I need to run, how many containers do I run.  So it's only using the capacity when it needs it, and then it shuts down and produces the output. Yes, Kevin. And once we start the workflow, the job actually, you know, submit to the back end for the back processing. So it's asynchronously. And to check the result, we can go back to our OpenShift and look at the pause here. And then you will see. Okay, so you'll see we just finished the page standard components, the first component. And it's completed, it's terminating right now. And move on to the next second component, which is test write OCR. And it's launching the pop right now. Okay. Also, if you don't want to use the OpenShift portal to check the status, you can also use our STK, which is the workflow status, you can check the status on here. Okay, so basically it returned all the component status in your workflow where they complete the page standard, the first component, and now it's running the OCR. The rest of the component is in pending state. Okay, so OCR is up and running. So we can go to the log and check the status of OCR. So we have two PDF documents processing here. So here is a refresh list to see when OCR complete and move on to the next component. Once all the steps defined in your workflow complete, you have got a final result in the loom format. And you can check what's the extended field result. Back here and OCR is still running. Right. I think at this point that cover of my demo. I'm just waiting for the workflow complete. Great. So again, just a recap on what we saw, we went over a little bit on why we decided to invest in in our overall night platform and I apologies. I did get kicked off a little bit there. We had a storm in the past through, but we saw why we would build Ignite, the kind of capabilities of Ignite. It's an open ecosystem built on containers, microservices that can plug into other offerings. Other cloud offerings, other data science offerings on prem and helps to manage the full and lifecycle deployment and production as well as model management of a model. Concepts built on top of the loom. So loom and loom out. It's easy for everything to communicate with that. And then at the end of it, it produces some type of output that's usually then fed to a downstream application. So there could be like an exception process where any of the predictions that need to go be reviewed go into an exception queue once I could feed through feed through in the business system to help make those business decisions. But that was I think everything that we wanted to show. And again, apologies for the a little often on there with connectivity.


Members of the 102nd Intelligence Wing, I'm honored to be delivering the command message on this drill weekend and on the 20th anniversary of 9-11. As service members, as Americans, it's a weekend with many mixed emotions, and it's a time to reflect. However, it's also a time to renew our dedication to service and to country. Likely you've heard many stories of the events on September 11th, 2001. Many of us watched the fall of the Twin Towers, the strike on the Pentagon, or the crash in Pennsylvania in real time. The seconds, minutes, and days afterwards are still vivid. We were aircraft maintainers, command and control specialists, intelligence analysts, logisticians, personnelists, and many other AFSCs. We executed our duties in real time against a real threat at home. It was monumental and life-altering, to say the least. We faced warfare on U.S. soil, enemies killed our fellow countrymen before our very eyes, and we were determined to defend our homeland from that day forward. There was chaos, there was adrenaline, and there was confusion. It was quite literally life or death as we were determined to ensure the safety of our friends and family. This weekend we'll have the opportunity to hear from those members of the one of second who demonstrated noble patriotism on that tragic day. In a moment the world changed. Deployments and mobilizations for service members changed, both in terms of location and frequency. America needed us, and because of our commitment to service, sacrifice, and country, we answered the call. Operation Enduring Freedom, the American-led effort to oust the Taliban regime in Afghanistan began just weeks after September 11th. With rapid planning on October 7th, 2001, American forces dropped bombs on Al Qaeda and Taliban air defense radars, airfields, command and control facilities, and training camps. We carried the message that acts of terrorism on our soil would not be tolerated. At that point the bond between first responders and our military was never stronger, and the global war on terror began. We fought hard in Afghanistan. Over 2,300 Americans were lost, and over 20,000 suffered wounds, many due to horrific improvised explosive devices. Personally I was deployed to the Kayak, which was located in Saudi Arabia at the time, during the first few months of the conflict. I remember briefing to operational commanders during the many days of Operation Anaconda and watching FMV footage as brave servicemen fought on the mountain known as the Whales Tale. Now as we acknowledge the end of our presence in Afghanistan, we may again reflect on how best to honor the 3,000 American lives lost on 9-11. We may wonder how to keep the memory of those who made the ultimate sacrifice over the last 20 years during overseas combat operations against the terrorists who were responsible for 9-11. How do we ever compensate for the years away from family and friends because of frequent and often difficult deployments, and how do we ever repay those 13 service members who died from a suicide bomb just days ago while helping Afghan citizens flee the Taliban in the final days? To me it's this, we stay confident in America's commitment to preserve freedom throughout the world, to stand firmly against atrocities of all forms and to protect our homeland. We honor the past by continuing to serve, by upholding the core values of the Air Force, integrity, excellence in all we do and service before self. We should move forward by pivoting from an emphasis on counterterrorism and ready ourselves for the next strategic challenge. We should support the national security and the national defense strategies renewed focus on great power competition with Russia and with China. We must commit to training focused on these much more sophisticated competitors. We should focus not on a single domain, but on operational concepts that integrate all domains, air, land, sea, space and cyberspace. We should embrace innovation and learn to employ technologies that allow for interoperability, all with the goal of strengthening our capabilities against any entity who might try to threaten our vital interests, the principles of freedom and the homeland of the United States of America.