Hey everyone, what's going on? I am Greg Salsman joined today by Jim Sonis of Fandall. He's here to help me break down week number 12 and give us some of the undervalued players he's looking at in DFS. What's happening, Jim? It's all good, Greg. This is a pretty tough slate as far as finding good value. So it's tough, especially a wide receiver. We could see some things open up later in the week, but as of Thursday, it's kind of tough in these streets. So I'm struggling a bit here. How are you doing? I'm doing great, man. What's not to love, right? Maybe we got DFS to help us win money for all the money that we lost in season long. So I feel good and you're going to help me win that money. And we're going to start a quarterback position where maybe it wasn't easy to find some value except at this spot where Derek Carr against the Jets at $7,400 is clearly a value worth taking. Yeah, Carr has been a good quarterback, like a real world quarterback all year long. His efficiency metrics are awesome across the board. It's been, you know, it's a larger and larger sample and Derek Carr has played really well. The one gripe you would have with him from a DFS perspective is that he hasn't really had a ceiling yet. And I think that's a good justification for going Baker Mayfield or Sam Darnold in the same salaried here. We talked about both those guys yesterday. And I think that there is a path to Derek Carr having a ceiling in this game, specifically because the Jets rush defense, even with Leonard Williams being gone, has been really good. What that will do is force the Raiders to be a little bit less run heavy than they have been so far this year. That gives additional volume to Derek Carr, could lead to additional touchdowns. And that's how you get a path to a ceiling game. Derek Carr hasn't had more than 21 fan goal points the entire year. So the ceiling has been bad, but the floor has been very good. And if we can talk ourselves into there being a path to the upside here, I think that it's going to be in a game like this, where they're facing a good rush defense and a bad pass defense. The Jets are a 20-fifth against the pass based on number of fires metrics. That is a great thing for Derek Carr. So I think, again, if you want to really shoot for the upside and that both Mayfield and Darnell, they're probably better plays strictly from that perspective, but Carr's floor is very good. And I think there is a path to a good ceiling here as well. So we haven't seen it yet this year, but I wouldn't be shocked if we see that ceiling come out on Sunday against this really bad Jets defense. I agree, Derek Carr has been a streaming quarterback of mine in season long fantasy. And in DFS, if you're not going with some of the stacks we talked about on yesterday's show, Derek Carr, a final alternative, especially in tournaments. Let's move on, Jim, get to the running back position where it is a bit tougher for sure. You begin in Buffalo where Devon Singletary's had a nice little stretch after coming back from his own injury. He's priced at $6,300 this week on Fandall. Why do you like Singletary and what should be a relatively tough matchup? Yeah, the 0.7 then been there for Singletary recently, but the usage has been. I think that that's really encouraging. With Buffalo being at home is a pretty decent favorite. I think that they're being favored by that much is understandable given that Brandon Allen is expected to start a quarterback for the Broncos. So I can see a path here where the bills are out in front and they can run the football. We saw that last week in Devon Singletary played 74% of the snaps. That has been his highest rate this entire year and that number would have been higher had he not lost two fumbles because he did lose snaps late in that game after that second fumble. I think that if we can get him to hang on the ball this time, we should see that snap rate be around 75% again. That'd be very good. Over the past three games, Singletary is averaging 14.3 carries and 3.7 targets per game. And no, he's not going to get all the goal line work. Josh Allen will get some, Frank Gore will get some, but he does have two of eight goal line carries inside the five yard line over the past three weeks. So Singletary's role has changed, but his salary has stayed, actually it's gone down. It's now $6,300 and 67 and then 65. So we're seeing the salary come down. And I think that salary is low enough to account for the inefficiencies in his profile, specifically losing that goal line work to Frank Gore. We know he is electric. We know he can bust off a long run or a long reception. I think at 63, he makes sense. I would be keeping an eye on other running backs here to see if other value opens up at this position. But Singletary we know will play about 70% of the snaps at home as a favorite. I think that given his workload being a combo of both rushes and targets, that's a pretty attractive profile for me. It is attractive and again it's kind of off the radar and I know this is what the segment's about. Devin Singletary price is $6,300. I don't know how many people, at least the casual players are in on what Devin Singletary has been doing as of late. To get him here against the Broncos like I said, not a match out you'd immediately point your finger at, but certainly something to consider on Sunday. Another running back that's worth taking a shot on is Miles Sanders against the Seahawks. He's priced at $5,600, which I think is a great price, especially not knowing necessarily the status of Jordan Howard. Miles Sanders could be the guy on Sunday, which certainly is worth taking a stab at at this price at under $6,000. Yeah, Jordan Howard still has not been cleared for contact. So we don't really know what his status is going to be. If Jordan Howard does play, you're probably not going to want to use Miles Sanders because they would go with the committee there, but if Sanders gets to be the top guy in this backfield again, I think that he showed on Sunday, even though he didn't really come through, he showed that he could be a really good play for DFS. His snap rate was around 85% in that game and he did a lot as a receiver. He ran 42 routes in week 11. According to Pro Football Focus, six of those routes came from the slot, seven of those were out wide, and that leads to really good usage in the passing game. We didn't really see that again. You know, we didn't see that in the game against the Patriots, but it was kind of a weird game. There was heavy wind, the Eagles were super banged up. Now they make it out on Jeffrey back and I think that bodes well. It is worth noting that Lane Johnson, their stud right tackle, looks like he's trending towards not playing, but they've had Andre Diller, their first round pick, working at right tackle in practice, which means I think things should be better there than they've been in the past when Lane Johnson has missed tons. I think that's a good patchwork there for this Eagles team that makes you feel better about Miles Sanders. I want running backs to get work in the passing game. Sanders should get that, and if he's into play, you know, 80% of the snaps again, he has big upside too. So for $5,600, I think he makes a lot of sense. Just gotta keep an eye on that injury report, because if Howard does go, Sanders loses pretty much all of his appeal, but if we get him as the top dog once again, I think there's a lot to like here at $5,600. Yep, $5,600 without Jordan Howard, a lot of appeal in this matchup in Seattle. I really like what Sanders can bring, and you know he's a game breaker, both in the running game and the passing game. You always don't look at just carries, you look at targets, you look at everything when it comes to the running backs, and I believe that Miles Sanders has the ability to do a little bit of all of it, especially at $5,600. You mentioned at the top, the wide receiver market was tough to identify and undervalued player in it this week. And we begin here with DJ Moore, one of your favorites and mine. He's in New Orleans with the Carolina Panthers, and he's priced at $6,400. Not super cheap by any means. Kyle Allen was awful last week. Why do you like DJ Moore? Yeah, even with Kyle Allen struggling, we've still seen DJ Moore paying off the salary even without touchdowns. His past three games, he has exceeded two X value at his current salary, at $6,400. He hasn't scored in any of those games. That's because he is getting monster volume. Over the past five games, DJ Moore has 30% of the Carolina Panthers targets, meaning that he has at least nine in each game. He has exactly three deep targets in all five of those games as well, which gives him big yardage upside. Now, the Saints may not have Marshawn Latimore. He was not able to practice on Wednesday. If you take him out of the equation, this Saint secondary did play well last week, but we're not expecting efficiency out of the Panthers. We want volume, and we're gonna get that out of DJ Moore at $6,400. So you're right. It's not super cheap at $6,400, but I still think we can classify him as being a value because his role is so much better than what this salary would indicate. So I think that he makes a ton of sense. There aren't a lot of $5,000 receivers I like this week. So I am okay, maybe spending down a little bit more, running back in order to get into the 6,000 range of wide receivers. There are some really good names here. I think chiefly among them though is DJ Moore, massive volume, pretty decent spot against the Saints as well. And it's gonna be really hard for me not to go to him at this number. I totally agree. No more Sean Lattimore, the key here for New Orleans against Carolina. DJ Moore in a blow-up spot potentially as long as Kyle Allen can get him the ball. And hopefully he finds the end zone. I know we've been waiting for that positive touchdown regression for DJ Moore all season long. Hopefully it hits here in New Orleans. One more wide receiver to get to, and that's Devonte Parker, your guy in Cleveland to take on the Browns. He's priced at $6,000, and there's been a real connection here between Ryan Fitzpatrick and Devonte Parker. We least expected it, Jim. It happened. Devonte Parker breakout is real. Yeah, it's been pretty fun to watch Devonte Parker finally have a really good year because he was super talented in college but just kept dealing with injuries. And this year he's finally been healthy and we've seen that talent finally shine through. And I think that he's in a pretty good spot this weekend too, with the Browns being super banged up. There's no Miles Garrett. There is no Larry Ogan-Joby. There is no Morgan Burnett. There is likely no Olivier Vernon. He missed practice again on Wednesday, which means the Browns will not have the guys who have gotten 20 of their 30 sacks so far this year, most likely. That is a big number. And I think it allows the Dolphins to move the football here. And when they've been doing so recently, it has been via Devonte Parker. The two games without Preston Williams, Parker has 10 targets in both those games. He has four total deep targets in those games, and Ryan Fitzpatrick, not the best quarterback, but he can connect on deep passes, which is given Devonte Parker upside. He went for a buck 30 last week, despite facing Tredavious White. Now he's facing a Browns team that does have really good and really fast cornerbacks, but they're depleted overall on the defensive side of the football, and he's just $6,000. We've actually seen Devonte Parker get at least 50 receiving yards in all but two games this year. He has had a really good floor. I think that all the injuries and suspensions for the Browns give him a good ceiling as well. I want to use Baker Mayfield and pair him up with Odell Beckham, but if I want to do that, I want to run it back with one of the Dolphins here and hope this game is a bit high scoring, and I think that what I'm doing so, Devonte Parker is easily the most logical route. Absolutely, it's a no-brainer here with Devonte Parker, and it has been fun to watch seeing him come into his own. If you just get away from Miami, you know, you wonder how he could blossom into, but we've seen it now the second half of the year. Devonte Parker, he's cheap and well worth taking at the wide receiver's position this week. Finally, we move on to the tight end, and this week, your dart throw is Dallas Goddard with the Philadelphia Eagles. Score touched on last week. Same game, of course, as Miles Sanders in Seattle has, you know, not been great against the tight end here. He's $5,200. I know more people will probably gravitate towards Dallas Goddard. What do you think about Dallas Goddard? Yeah, Dallas Goddard is a good play, too. He is $6,100. I think that is too cheap. So if you can get to Dallas Goddard, I would recommend you do so. His volume has been awesome, but the volume has been good for Dallas Goddard, too. He has a 17% target market share over the past five games, and if you do exclude that one game where they played it out at Alshon Jeffrey, it's still 17%. That is a really good number for a tight end who is down in the low $5,000. He has had plenty of red zone chances in that time as well. And Seattle's defense is trending up, but there's still a lot of shutdown unit we need to fear. And you look at this Philadelphia Eagles wide receiver corps, not only is Alshon still banged up, but Nelson Aguilar missed practice on Wednesday as well due to a knee injury, which he looked like he sustained on that attempt to touchdown catch late in that game against the Patriots. So we could have, again, steady volume available in this Eagles offense. That bodes well for Dallas Goddard, who is $5,200. I also really like Greg Olson. He is $5,100. I think that, again, we have concerns around Kyle Allen, and those are justified, but Olson is beginning a lot of volume at $5,100, where he makes sense. Ryan Griffin is $5,600. I could go there. Mike Gisicki is in the low $5,000. He's gotten good volume, but Dallas Goddard has a good quarterback in Carson Wentz, who has struggled so far this year, but he's been good when targeting Dallas Goddard. And I think that we can go to Goddard once again. If you have the salary, I do like Earth's a lot, so I want to try to get there in cash games, but for tournaments, I am very okay dropping down to Dallas Goddard and plugging him in at this low salary. Yeah, Goddard works out great. Greg Olson, a fine choice as well. These prices are good at the tight end position. Before we go, though, I want to throw one more name out there. Well, my man, Big Ben Watson, with Mohamed Seneu and Phillip Dorsett banged up. Or he's got to throw to somebody. Yeah, I could see it. Kind of hoping he throws to Nikio Harry, because Nikio Harry is $5,000, and that'd be a great value-wide receiver to have. Ben Watson, though, has played 70% of the snaps and I believe now three or four straight games did drop a bit last week with Matt Lacoste being back, and that's a slight concern for me, but Watson's in a very fast game. It's the first and third ranked teams in situation neutral pace per football outsider, so I don't hate it for sure. I think that he's very interesting, but there are a pretty good number of low salary tight ends, but I'm keeping a close eye on Nikio Harry with Phillip Dorsett and Mohamed Seneu both banged up. This is why Jim's the best. He had no idea I was going to ask him any question about Ben Watson with Patriots, and he just rattled off 74 stats in 30 seconds about Nikio Harry and Ben Watson. This is why he's the best, man, for real. That's going to do it for us here in the FanDuel Hurry Up for Jim Sanis. I'm Greg Sussman. Gabe Marancy joins me tomorrow as he gives us the six best bets for week number 12. Have a great night, enjoy the game, and we'll see you tomorrow.


Thank you. Thank you all for joining this morning. I know it's early for some. My name is Kevin Martelli. I'm a principal advanced technologies cloud leader at KPMG overseeing a lot of our cloud innovation ML ops and pipeline. So nice to meet everyone virtually today. Yeah, sure. Thanks a lot, Kevin. I think so. Thanks for joining the session today. My name is up and up Joshi. I'm a senior manager in the open shift product marketing team at Red Hat. I have over 20 years of industry experience in a lot of roles on both the customer side as well as on the vendor side in my current role at Red Hat me and my team. So we focus on building out and being able to evangelize the value of Red Hat open shift industry leading Kubernetes platform with integrated DevOps capabilities for cloud that is workloads. That also includes emerging technologies like data analytics, AI, ML, and so on. Back to you, Kevin. I'm looking forward to the talk today. Thank you, Evan. So before we get started in today's talk track around ML ops, I wanted to give a brief background around KPMG. Many of you may know KPMG is one of the big four consultancies and advisory firms. And within our advisory practice, we have a group of practitioners around 25 finders so located in multiple countries where we really focus in on maximizing the value from data, AI and emerging technologies. We do this both from our internal enablement as well as we support our clients in their journey in the space. In addition, we have partnerships with many core technology partners, you know, IBM and Red Hat and Microsoft and Oracle to name a few. But these ecosystems allow us to again accelerate our clients journey in the emerging tech space. So today, the goal of this talk track is around ML ops and the way that we want to show you what ML ops is and how KPMG solves for internally, as well as how we solve for it with our clients is we're going to be doing a deep walkthrough, both at a business process level as well as a technical level of our patented approved platform called KPMG Ignite. First, we'll give you a little bit of a background around what KPMG Ignite is. We'll talk through, you know, who is using Ignite, why they're using it, and then we'll drill into some specific challenges Ignite is used to solve for. We'll also talk a little bit about use cases and finally we'll get into the meat of the presentation around how it might solve for the problem of ML ops, both from a process perspective, as well as a technology perspective. So KPMG Ignite initially was built to extract value out of unstructured data. So we saw the need with unstructured data sets, predominantly contract documents, loan documents, emails, voice, etc. But there's a big need early on in the days of machine learning where values need to be extracted out of unstructured data. And that's really where KPMG Ignite started its generation, if you will, and over time it took in unstructured data as well. It's a platform that's built in a modular form, really based on open source, with the containerization such as OpenShift running it, and open architecture. This gives it the flexibility to above over time and take into tooling as the market demands. And finally, who's the platform for? Initially it was for data scientists and engineers, but you'll see a lot of hooks where business users can come into play and also integrate within the platform. So Ignite solves many of the end-to-end challenges that enterprise face from taking POCs or pilots into production at the ML use case level. However, we think about the ops perspective, and where does ML ops and what types of challenges does Ignite solve? It solves through different areas, whether it's pre-deployment, deployment, and post-deployment. In the pre-deployment space, it provides a robust set of tools, AI tools that is able for data scientists to run their experiments, enabling for risk management teams to see the outputs and the results and explainability of models. It's ready-to-scale training infrastructure, being on built-in-topic containerizations that allows you to scale out on your training needs, artifact and version management, has the ability to store the metrics associated to models, and finally, there's some new feature sets under development around explainability and surrogate or alternative models that can be used to get through your governance processes. In the deployment phase of Ignite, many things it solves for the models as well around automatic infrastructure provisioning, heterogeneous model deployment environments, multi-tenancies, and again, the hook into your model risk management and governance processes. And then finally, around the post-deployment, some of the more important things here I think are the logging and alerting after the fact to keep track of the model, the scalability to be able to scale the platform on demand based on the specific needs, the integration into a CI-CD pipeline, and overall performance monitoring and health metrics. So Ignite itself is the concept of Ignite is to build use cases and or solutions. Use cases and solutions are really built by stringing together what we call components. You can think of a component as an individual piece of work or functionality to achieve some type of solution or use case. A component can almost be anonymous to a microservice. Components can be something that is custom built, maybe something KPMG built and their IP is there and that could be maybe an anomaly detection model here at the bottom. It may be something that is an open source type of capability in the marketplace today, maybe like Tesseract for OCRing. Or it could be something that comes from a third party like a proprietary type of algorithm, something maybe for speech to text that you may be able to get off of the CSP provider. But at the end of the day, the components are the smallest piece of work effort that strung together into a workflow will produce a solution. And all along the way, the ability for the human in the loop to be able to evaluate the outputs that's coming out to determine if the models are in fact predicting accurately or over time making changes so retraining can go into the process. The human in a loop also extends to other groups in the organization. It could expand to the governance team that needs to see that the metrics that are part of the model can expand to the operational team for deployment and the health of the system and we'll go into some of those details. But again, at a high level, the solutions are made up of many components that are individually pieced together into a workflow that will then produce an output. So ignite itself solves for many different types of use cases. I'm not going to go into each individual use case that you see here on the slide. But as you can see, a lot of these use cases are around unstructured data. The first three are more around contractual terms and PDF documents. The last one is around, you know, KPMG intelligence interaction, which is that the chatbot types of interaction. I'll take one use case here as an example in cognitive contract management. One of the challenges that we see organizations facing is that they have a ton of contracts out there and being able to get the right information out of these contracts to make the right business decisions. So for instance, are vendors compliant to terms and conditions? Are payments being paid on time? Any type of information that you think is relevant as part of, you know, your contracting process. If night can help break the contract down to usable pieces, that will ultimately give you an answer to be able to determine the next steps. Similar for LIBOR analytics, the LIBOR rate is going away shortly and there's a lot of needs to change these rates in documents. What are your alternatives? You have to understand the documents and figure out what you can replace the LIBOR rate with. And this is another one of the use cases where it breaks down into components, maybe OCRing a document, you know, breaking it down into sections, using some rule-based inferences, maybe using classification models to determine what type of options are available. And as we mentioned before, you know, open source and Red Hat are really the foundational pieces for what makes up the Ignite platform. And open source was really chosen because of its, you know, cutting edge algorithms, its ability to scale massive models, you know, I think we can all agree to the innovation that takes place. So foundationally, open source was, you know, a core tenant of what the Ignite platform needed to be. And then partnering in conjunction with Red Hat, Red Hat provided it the containerization environment, which gave us the ability to scale massively, consistency anywhere, enhance security and controls, as well as the robust CI-CD pipeline. You put these two together as part of the overall, you know, Ignite platform and the foundational pieces that run it from the containerization. It's an achievable path that we're able to bring AI applications into production in a quick and fast way. I think a lot of us have experienced the challenges of creating a pilot or a POC successfully. But you'd spend months and months getting that POC into production because there's a lot of processes and controls and things that may not have been considered specifically around the model, the ML ops management. And we're going to dive into the details specifically, you know, how it might sauce for that. But before that, share a little bit of information around Red Hat. Thanks a lot Kevin. Before I talk about OpenShift, I wanted to talk about the fundamental value that containers, Kubernetes and the DevOps operational practices provides for the AI ML workflows. The technology and the operational best practices provide the much needed the agility, flexibility, portability and the scalability for the data scientists to build, to develop, iterate and share the models. It appears in a seamless way and as well as with the software developers as well. And then for the developers, so they get the capabilities to be able to do a rapid coding of the software apps that are powered by the machine learning and the deep learning models. And the data scientists and the developers no longer have a dependency on IT ops for every infrastructure provisioning task. Next slide Kevin. To execute on the AI workflows, what you need is a bunch of software tools such as say TensorFlow, Spark, PyTorps, Jupyter, Notebooks and so on. And the data services like the data streaming technologies like say Kafka, SQL and NoSQL databases, it could be object storage and so on. And then what you need is the end-to-end solution architecture. It may span across on premises in the cloud and it's all the way to the edge for the various needs, such as the security and compliance, the data generation at the edge, the data gravity and so on. And all these tools and access to the data sources, it should be ideally supported on a self-service hybrid cloud platform that is able to encapsulate all these infrastructure endpoints and also provide the consistency and the scalability anywhere. Now the hybrid cloud platform should have the integration with the hardware accelerators such as the NVDA GPUs to be able to speed up the data analytics, the model training as well as the inferencing activities. Finally, the hybrid cloud platform should be able to offer the consistent experience across like on premises, public clouds and the edge and be able to efficiently manage in a unified way by the IT operation. So OpenShift built on the containers, Kubernetes and the DevOps principles is the industry leading open hybrid cloud platform that has been chosen by many organizations, such as KPMG, to be able to provide these capabilities and be able to accelerate the AI ML life cycle. It has empowered the data scientists, the data engineers and the developers to be agile and be like very collaborative throughout the AI life cycle and without having dependency too much on the IT operation or the individual activities. Kevin, can you go to the next slide please? Yeah, so OpenShift provides like a lot more than the fundamental values that we have to get with the containers and Kubernetes. The first thing is it actually simplifies the deployment scaling and the life cycle management of the containerized AI ML tools such as the few of the examples that you see on the screen here and a lot more. By being able to automate the day one to two operation task that are associated with these tools and this helps ensure the high availability and the faster time to value. The second key thing is integration with the hardware accelerator such as the NVIDIA GPUs using the Kubernetes operator concept which ensures that the modeling and the influencing task can seamlessly consume the GPUs for the data scientists and the data engineers directly from OpenShift. The third key thing is OpenShift, it comes with a self-managed as well as the cloud-hosted option and this actually helps you get a consistent way to be able to perform the day one to two ops, be it on-prem at the edge or in the public cloud. And also providing the much needed portability and the consistency of the modeling and the app-dev workflows for the data gathering, the preparation, modeling, the deployment and the influencing task. And then, like I mentioned earlier, OpenShift also comes with the integrated DevOps capabilities. That way, it actually helps extend the value of DevOps for the NTR machine learning life cycle and that helps the collaboration between the teams. And this helps ensure that the model can be easily deployed in the app-dev processes and the rollout of ML powered intelligent applications. And then finally, OpenShift is a fully integrated hybrid cloud platform. And it includes all the key capabilities like the monitoring, automation, the DevOps tool chain, the pipeline, GitOps and so on. And all this is built on 100% open source technology. And this helps drive the innovation without having the locking. So back to you, Kevin, to dive deeper into the night platforms. Over to you, Kevin. Thank you, Evan. So as we kind of set up a little bit of the lead in now, we're going to dive into some of the technical details on how we are solving, as I mentioned, from both the KPMG perspective as well for our clients around the entire MLOps life cycle. If we look at the platform itself, the KPMG Ignite platform, it's made up of many what I would call sort of your infrastructure components. And these are things that you'll see up here at the top box here. And these infrastructure components are ones that are continuously running on the OpenShift platform and are intaking the use cases or the components that string together to form a use case from an execution perspective. I'll call out some of these components that I think are relevant to the overall understanding of the platform. But for the rest of the presentation, we're really going to be spending most of our time around the model management capabilities of the platform itself. As mentioned, if you look at the bottom layer, this is right on top of Red Hat OpenShift from a containerization and orchestration perspective. We're going to have an all case of mantels around the advantages of using OpenShift and these types of solutions. In addition, the full and embedded CI CD pipeline is there. And then we also have an abstraction or a layer called MinIO, what we're using from an object storage perspective. So it's a technology layer that achieves the same level of object storage that you would see on cloud providers for quick access and read and write times. At the component level, there's a few things again that I think are important to call out. One is the workflow engine.  So it could be a data ingestion, document classification, some type of data extraction. These types of components come together to form a workflow and they can split off or they can be a linear based on your use case. And as these workflows are executed, the output of that particular solution is generated and everything is coming through, which is a restful service of the Ignite API. So a workflow can come in through the API to say execute a certain workflow or the API or the UIs that we have can also come through the workflow. Some of the other components are important parts. I think again are the model management pieces, which I think we'll drill more into later on around how do we store the model metrics, how do we deploy the models. How do we get explainability and surrogate models? How does all this come into play? So it's not only the technology enablement, but it's the business process. It's the risk processes and it's the business users integrating with the end-to-end process to make this successful. And we'll go into how that works. There's the message controller, which is Kafka. So as components are done in your particular workflow, it notifies Kafka that this component is done start the next one. And the nice part about using the OpenShift platform is that each one of these containers can be spun up one or an amount of time. So if you have a container that's a little bit heavier, maybe an OCR container, you might want to run 50 instances of that where if you're doing something with less compute, maybe it's only a few instances of that container. And within the ecosystem, the Kafka sort of keeps track of the messaging and know what container needs to go next. As I mentioned, there's coder development environments through notebooks, log and storage, elastic search, storing some of your model statistics after it. And then if you need time series databases like Permeteas that may be relevant for some of your model, modern processes, and then ultimately ZooKeeper. But this is the component level view of what the Ignite platform itself looks like. And then one more view we'll show you of the overall Ignite platform and then drill into the pieces of the ML Ops is the platform itself, you know, as mentioned is everything comes through our authentication or API gateway. The API gateway can has two ways it can come in. One is you can come in as an end user through an interface, which has a lot of interface controls that we'll talk about interface applications. And then the other way is through a RESTful service. When you're sending a particular workflow through the RESTful service, think of the workflow again as being a strong and together of components. Those components come into a particular order of how that workflow needs to execute. That workflow is stored inside of a database in Postgres as a metadata. And then as it's stored in that particular database, you know, it comes through and it will then start to actually execute and expand out on the ecosystem. So the Ignite component builder understands the workflow, understands the components associated to that workflow, and then we'll pull it out of the Artifactory or your registry or Artifactory registry and deploy those components onto the platform. And again, those components are completely dynamic based on the need for that particular use case of how many times that component needs to be instantiated. If the component has a model, it will then go through through the model manager, it will treat the model that sits in MLflow and pull and load the model into that component. And again, it can launch that component one to any times based on again, how many documents or how much data needs to go through that model for prediction. And outside of the platform, you'll see it's right on top of OpenShift. There's orchestration capabilities that may need to be put in the place. And then where is the consuming documents or the consuming input for the process and then the consuming output. Now getting down into sort of the meat of the overall topic area and conversation is what the talk track was around MLOps. Now, when we think about MLOps, MLOps has many different functionality that's needed to be built, such as model training, model serving, you know, MLMAT, all management. In addition, there's a lot of business processes that also come into play and we're going to drill into each one of these content areas.  There needs to be a place for your governance team to understand the model attributes that are being generated. Are they aligned to kind of expectations? Is it a bias model? Are you getting the right, you know, output for what the parameters need to be? Then there's the serving part. I need to serve this model into the ecosystem. How do I serve it? How do I consume it? Can I serve one model? Does it can be multiple models based on scale? And then finally, the management part of it. How do people come in in management? And again, how do I create training data? How do I evaluate the model results? How does my governance and risk team get comfort that the model, in fact, is appropriate? All these different pieces need to come into play to have your full end-to-end model lifecycle. So if we think about model training, Ignite supports two mechanisms for model training. The first mechanism is you can bring your own model. So maybe you built your model somewhere else and you want to bring it into the Ignite ecosystem.  So Ignite has Jupyter Hubs. It instantiates a notebook for each individual developer. A developer can then use that notebook to build those models. Those models have a wrapper layer that we call Ignite Model Manager, which is a custom-built component. That model manager functions in two ways, whether you're bringing your own model in or if you're using, you know, your own notebook in the platform to develop it. The Ignite Model Manager can then send the relevant attributes into MLflow. So things that you may want to capture in this metadata is very flexible. You know, some clients want to capture the training data, so there's metadata to where the training data was. If you're running classification models, you may want to capture the accuracy, you know, the precision, the F1 scores. If you're running regression models, maybe the mean square error. So based on what you're doing and based on what you want and based on what your governance processes are, a lot of different metrics can be captured as part of your model. All these metrics and all the data in the model itself is all, again, persisted in a persistent volume via the MINIO, and they're also backed up into Artifactory. Another part that the Ignite Model Manager also does, which is currently under development or enhancement features, is there's a lot of ask that we're seeing from our clients to have explainability. You know, what is going on in the black process? I want to get confident that the information that's coming out of this is in fact stuff that we're expecting, or how does our risk processes gain comfort through into production? So explainability, we see that as becoming a pretty big topic and adding capabilities and features into that. Another thing is model alternatives. Are we using a very complex deep learning model? Can we replace that with something less complex and still get the same level of accuracy? These are all things we see that the business is asking for and how they can get these models moved into production quicker in a more governed process. The next is around model ops and model serving. So how do these models get served into the Ignite platform? We saw the high-level flow on the prior page, but just to kind of walk you through the use case, as we mentioned before, a workflow is made up of components, and components strung together in a workflow is a solution or a use case. So if we take this particular example, there will be some type of service, whether it's an application or a user that will initiate a workflow. They'll say, please run these components in these ways. The workflow will come through the Ignite platform, and this is an example of maybe a demonstrated workflow where we're doing OCR, we're doing template matching, machine translation, et cetera. This could be anything that you want to do as part of your workflow, data extraction, anomaly detection model, et cetera. But the idea is if it comes at a time, and these are all containers, so part of that process of the deployment, which I think makes the OpenShift platform and the containerization very unique and quick, is that these containers are sitting inside your artifact or your registry, and only at the time of workflow execution do these containers get pulled down into the platform to run, and they scale based on the needs of that particular function, if you will. So let's say, for instance, you're going through your workflow. These are the components. Your containers are being pulled out of your registry. You're having an OCR component running, maybe one to end, your template matching, machine translation. All these components are running. They're notifying Kafka back and forth that they're complete, and the next component is picking up on that activity. Now you get to a point where you need to run a classification model. Well, as part of this classification model run, what will happen is through this Ignite component or Ignite model manager, it will integrate into your MinIO where your model is stored. It will have the right JSON. It will pull the right value, and then it will pull the data through into your component and then load it into your component. And your component for this classification model, maybe it's one instantiation or one container, or maybe it's 100 containers that need to run. Based on that, you're loading the model in real time into your classification container, executing that, and then you're passing the results to the next step. In this case, it's data visualization. So the main areas or the main points of this is you're getting real time model loading during runtime. So it's picking up the most recent model based on your parameterization of what model version you want to pick during the actual runtime of the use case, which can change your scale model based on the needs. And finally, it's outputting a lot of the model metrics as well as part of your runtime evaluation. So any metrics that you want to capture can be stored as JSON, can be stored in a time series, database like Prometheus based on the types of models, but all these metrics are going to be stored. So over time, you can review those metrics to determine what type of changes you need to make. Now, the next part around here is what I think is one of the most important parts, which is the ML Ops management. And the ML Ops management is the dashboarding area where many different types of business users can come in to evaluate the process and the end. And we talked through some of the ability to train models and serve models, but that's probably a very small piece of the puzzle. Once you get done that and you want to get something into production, there's a lot more work that needs to get around that. So what is this management console that is put together? So some of the stuff that it does is it helps provide the businesses to create training data. So training data and labeling the data as we all know is a huge problem for businesses to do. There's different techniques on how to create this label data. Some companies like AWS will actually create it for you, but this is a big problem. But there's hooks for the business to create this training data. Also, as we talked about earlier, there's a lot of challenges in the governance process. What we traditionally find is the governance teams will take legacy ways of approving models, maybe past financial models, done in different tools, and apply those same techniques to these more advanced models that may not be trying to solve the same business problem. And sometimes the governance processes become a little bit more challenging. So it's good to understand the types of metrics and data points that they want to capture. So that's all part of your end-to-end ML Ops lifecycle. In addition, we're seeing a lot of wants and needs around how these models are working for the governance team. Can you provide ways that the explainability can gain better insights so they get better comfort on how these predictions are actually occurring? And maybe there's not biases in our algorithm or our data is not skewed. So the explainability is becoming another very critical piece for how the governance team wants to work with that. In addition, there's the pieces where the evaluation of the training results. So the businesses have the opportunities both during the training time to say, you know that this is right or this is wrong and update the new value to give to the data scientist. And then also over time, right? Over time, as you're looking at the model's predictions and the different scores and metrics that you're capturing, it's important for those business teams to also be able to understand that. So over time, they can feed it back into the loop to be able to get better and smarter models through that process. And this next slide here, it talks a little bit about what many of us have probably seen as a traditional CI CD pipeline. And probably not a whole lot here, I would say is different than what you've seen in before. You know, the ability for developers to put their information into some type of repository like GitLab or Bitbucket, having the ability to trigger it through something like Jenkins and build it if you're using maybe like Red Hat OpenShift to build it, to store that container image to go through the right scanning processes, testing, et cetera. But one of the interesting points I wanted to call out over here to the right is the CD part of it. So as the Ignite infrastructure, the CD part is pretty straightforward as we talked about before. You have the ability to deploy Kafka or Postgres or different componentories into the ecosystem. But there's also a deployment. Probably not your traditional CI CD, but none of the containers and none of the components that make up a use case are deployed inside Ignite until runtime, until someone calls that particular workflow. So once someone calls that particular workflow, this Ignite component builder, what it does is it reaches out into the Artifactory or into JFrog where you're storing your information. It will pull that container. So say it's pulling a classification model. And then based on pulling that model, based on the workflow inputs, it will know how many of those containers it needs to instantiate onto the cluster. So again, it could be one the end number of containers that are running that model or that component to run. And the Ignite component builder is continuously deploying images that are or containers that are part of a workflow to run on top of the environment. So I think this is a pretty good aspect of the platform and that we're not constantly running these different things, but really only when it's required and when the workflows are needed. So again here, you'll see just some traditional tools around scanning, storing, triggering the process and then how we're moving both from this code repository to the registry into the environment. Okay, so with that, I just wanted to kind of touch on a few of the key takeaways that we found important as we went through this. And one is, we kind of hit on this a little bit too, is make sure your business processes keep up with the technology needs. And what I mean there is that we have a lot of processes today that businesses are working against that help them move models into production. A lot of these may have been more traditional financial service types of models that aren't necessarily the same as the type of models that we're building today. So it's important that as we're building these new capabilities, we still have the right governance and controls in place. But how do those business processes evolve just like the technologies themselves are evolving? And sometimes we're pushing legacy processes against newer ways which can cause a little bit of a challenge. The second one here to call out is containers, Kubernetes and the DevOps powered by a hybrid cloud. And what this means is that there's so many places we can build these advanced analytical models. We can build them on-prem. We can build them in any number of CSP providers. How do we keep an ecosystem that is easy and quick to bring something from pilot to production? Well, through containerization, through OpenShift, you have the ability to have a consistent way of deploying these models in any environment that you're using, whether it's a cloud or on-prem. And this third one here is one that I like as well. It's to leverage an open and layered architecture. And what we mean is that, you know, as we know, every day there's a new piece of technology coming out and a new tool that we want to test out and try. How do you keep your architecture flexible enough and open enough that you can plug and play these new capabilities and at the same time expand out your architecture? Because you're never going to get everything the first go-around. It's going to need to expand to have new capabilities. So being flexible and open to be able to plug and play new component trees, as well as being layered so you can build upon the foundation is really important. And the fourth one here is the integrated training and up-skill of technical business teams. So these are newer technologies, newer ways to operate. Kind of goes back to point number one, but training your teams, getting your teams up to speed. And it's not just a technical side. You know, it's really important how the business teams align to this as well. So with that, I don't have any other slides. I don't know if you have anything that you want to add. So I think that was great. I think there were a couple of questions in the chat. Let me agree those out. This is a question from Praveen. And the question is, do you have a security layer on the model saved in MINIO? Do you use a few specific formats or extensions to save the model? For example, dot pickle format is not as advisable due to security reasons as against ONX, et cetera. It's a good question. I would say that that's something that we could probably enhance upon from the security paradigm. A lot of these are run as batch processing. So there's controls on the outside, but the RBAC controls themselves specifically in the model specifically to MLflow is more controlled through the ignite pipeline. And then through the model IDs that are stored inside of MLflow, we're not using native MLflow capabilities from an RBAC perspective. But I will say it's probably not as secured as one would want in some of the enterprises. That's a good question. Cool. Thanks a lot, Kevin. There is one more question in terms of MLflow. Is this MLflow from your Databricks? Got the question. That's a really, really good question. So this is not the MLflow from Databricks. That's only available through the Databricks ecosystems on the cloud. And one of the challenges that we experienced with MLflow was the ability to serve models. It's not very robust to serve the model into the production pipeline. You may want to use something like Qflow or as you've seen, we created a custom connector, which we call it a connect, which is leveraged to serve the model. So it's not one another the same. Databricks did put some hardening around MLflow and does give you the flexibility to deploy those models in production via the MLflow and the Databricks enhancement. But MLflow out of box doesn't support the deployment of models easily in a scalable way. Cool. Thanks, Kevin. I think these were the key questions that I saw in the chat that were not answered. So I think they're good from the Q&A perspective. Thanks a lot a lot all. I hope you learned something new. So Kevin, do you want to say anything to kind of close things out? No, I just really appreciate everyone spending the time here. And like I said, I think our journey around ML ops, AI ops, whatever we want to call it has evolved considerably. First, it was really just kind of getting the model out there and embedding it into our code base, which really didn't become, which really wasn't advantageous. And then we evolved into what we had today. I think by doing this and doing it in other large organizations, we find it meets a lot of the needs. However, I see where the biggest gap in this whole entire process is still around the explainability of AI and how well can we explain it to give comfort over the regulators and the governance teams to move some of these models forward. So I see that as being one of the biggest challenges still yet to solve.


It's 12.7 miles away and guess what? At minimum we were 83 cents. I don't have money to pay the rent. Just so you guys know, if you want to sign up for delivery, all you got to do is click the link in the description and sign up, baby. Also, guess what? I just found out this morning, baby. For the last three orders we did, we received $114 for three orders to get the order, baby. Come up below and let me know if this is good or not. We are here at Red Robin's. We get to this money when you stop and let's go. Man, baby, we want to get everything situated. We're going to actually the same, one of the same places we went to last time, I was going to go out to the end of time that I think it's like a doctor's office or no, it's a medical, uh, medical place or something like that, baby. I should ask him that he got medical care. So we're going to talk about getting some of that money. But the setup, they have, they just having a big meeting and everything's right on it. You know, ask all them type of questions. Y'all see what happened with my guy in here? When my guy was here, they're going to be sitting right there, baby. Well, he ain't here. Everything goes a lot slower. Hopefully they got this order ready so we can get it done and get on the one and get these phones. So we can get that $32. We hollow and pop our collar. We have to brought him in getting it in. We got the chicken for the cocktail. Okay. All right. Thank you, sir. Get it in. Oh, he things over here. Hopefully this is it.  I had my foot up. Oh, no, it's something about that new vibe that I don't really, you know, already I don't keep, I'm keeping it real with you guys. I don't like his vibe because you're wrong. He seemed like he had a little attitude problem. It ain't my fault. It ain't my fault. It ain't my fault. So they got $300 and some dollars with the stub. So, oh, see, this time they was smart. They actually ordered the whole, the whole setup because before they had it separate. All right. Let's back. So this, this is it right here. Yeah. Thank you very much. So I appreciate your help. Y'all have a good one. So these are they gonna come back, come back and get the other ones, baby. So last time they ordered, guess what? They only ordered, they ordered like a big thing of chips and some hamburgers, but a lot of the orders were mixed up because of, you couldn't see the writing on everything. So I'm talking about they squished it up. It's probably a good idea if I ask my business partner to do this part because guess what? She be packing them joints for righteous. So with five bags, I got one right here, grabbing another one from right here, laying back. I wonder if that guy is the owner of something. I ain't never seen him before here. So, you remember, maybe he was a new manager or something. He definitely interviewed people. So we fly high, no lie, you know this. Come on, baby, squeeze in there, squeeze in there. There we go. You got like maneuvered a little bit. Pull the wrist out a little bit. In the game, coach. Close this joint up, hold that joint, set up something right there. So one of those, I think is our art of utensil. So we have two more bags we're going to get. The one that's utensil, I could just put it in the back seat, keep it running like a track. I'm gonna leave that one right there for now. Pull on this joint and get it on. We fly, no lie, you know this. I don't know about that. I don't know about that. Y'all don't know about that. That is a banger back in the day. Guess what I'm pulling out. Pull off that wagon. We ain't bragging. We got the wagon. Anytime out. How you doing, sir? I'm living the dream. I can't complain besides the rain. I hopefully doesn't keep on going. Yeah, look. Yes, sir. That's what I'm talking about. You have a good one, sir. I'm gonna put this right here. Bam. I'm gonna grab this, John. Last time it came out and helped me. Put that like that. I'm gonna hold this one back. Put that in the pocket. Rock it in my pocket. I'm gonna pop it, but y'all know about that. Come on, let me know if you know about that. I told you about that song before, so y'all gotta know. Come below and let me know if you remember that, baby. We're gonna be smooth with this, John. Slide this, John, in here. So, again, last time they told me that they make medical supplies, so I don't know if they got some medical care in all that stuff. I see you. Hello. How you doing, sir? I have an easy catering order to deliver. Lord, don't get me. Don't get me. Okay. Okay. Yes, sir. I missed you last time, I think. Oh, yeah. Last time when... I thought this was all gonna be boxed lunches today. Yeah, so it's way easier. Yep. You might just deliver to the front desk, but that's okay. Whatever you guys want to do is fine with me, sir. This works. So, do you guys have these lunches often? We've been doing a whole lot of training here, so it's been pretty often. Let's start right here on packaging things and Kim will be back in just a second. Okay. Everything situated? Okay. That sounds great, sir. So, does that mean you guys are hiring a lot of people or just... Really? Congratulations. I mean, I guess it's a good and bad thing, because business is good, but you got to do more work, too. So, what do you do here, sir? Plate operation. Oh, you can use jack-all trays, huh? One of everything, huh? Well, I'm sure you're busy all day, so that's a good thing. Let's make the time go by fast. She might be... She might be just putting me some hot salad. Sure. What do you got going there? We're back here again with... I don't know what that is. It looks like maybe a hamburger. I don't know why they're not labeling these things, but I'm quite sure that most of them probably should be the same thing. That's what I'm guessing. She was getting a list, but... Yes, they didn't give me. I don't know what they gave me. Hello. We meet again. Of course they don't. They make sure it's all harder. I'm just looking at these trying to figure out... It looks like it is a hamburger. So, there's supposed to be 10 gourmet burger boxes in there. So, that's 246. So, you kind of see... Yeah, the yellow, the yellow, they're kind of white. That's 246, 8, 9, 10. Now, let's just get into something that's marked. This has cheese, and I can see the cheese in there. So, it's a gluten-free box burger. So, the three salads are there, and that is good. And these are wraps here, and chicken here. Wrapped in chicken, at least. If I can get it out. Thank you very much, sir. That's one. That's two. That's good. You got the chicken. These are the cookies over here. So, we got two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14 of them. We got veg. Is the veg gluten-free? They one and the same? Veggie patty. Yeah, gluten-free says veggie patty. So, we got one of those, and then we've got two wraps. No idea. The wraps aren't on this order here. So, it looks like they added some extra wraps and some extra burgers. Three, four, five, six, seven, eight, nine, ten, 11, 12, 13. Looks like we got 14 burgers, not 10. We have 16 to 18 people in that meeting. That's why I said she didn't order a lot of extra. Yeah. So, I think we're good. Looks like it. I hope so. Yeah. It looks like we've got at least what was on the order that we were told was going to be here. So, and it doesn't seem to be about who's got cheese and who doesn't. She told me to go buy cookies. That's what I did this morning. No, she had those. She's like, if you want to go buy cookies, I will. More cookies is more better. For more than better. You cannot have too many cookies. Exactly. Too many cookies and not enough label food, huh? Yeah, right. All right, man. Thank you. I'm good to go. I'll just take a photo really fast, so you guys don't mind. All right. And that is everything. Thank you guys very much. Enjoy. Don't work too hard. Yes, sir. Keep on grinding even if you lose. Loot is a company 0.7 miles for $6. We're here to pop our collar. Let's go. We are. I'm doing great. I can't complain. I'm still 11. Oh, this is a doordash. Yeah, it's for you two. Do you feel like ordering a bus? Yeah. Oh, this order is getting ordered. Here you go. You have a great one. Order number three. I got a question. Why do you hate 0.6 miles for $6.75? Oh, my God. I ain't even here, man. My God ain't here to give me work. Hopefully, I got a schedule to go up to the cheddar where it goes. You know what I'm talking about? Yeah, for Shanji. Yes, sir. Got a Swarovski, baby. Here's over this pot, Betty. Order to get it in order, baby. You know what I'm talking about? That's pot, belly, order to get it. Oh, they got a, they got an Amazon job right here, baby. And it says do not run the doorbell because they got the family in their sleep. Hit them with a photo. Let's go. Order number four. Why's the people that be hating them are low? Mariano's four out of 4.41 miles for $17. We just came out of here. We are hot. Let's go. We're here to join with this Mariano's order to get an order. Look, it's only one bag, baby. That's for my business partner, so they add another thing at the end. She checked out already. They ought to pocket, but it is what it is, baby. Jumping like it's a hey, doggy doggy. No doorbell. Let's go. Order number three. They start telling lies. Panera bread, I'm 25. Panera bread to get this bread. You heard what I said. And it's rain. It ain't no thing, but a chicken wing. Come on. I'm going this y'all. See if they got us together. This home. Oh, we got both of them right here, baby. One, two, let's y'all see this rain. I'm talking about this rain blowing, blowing, baby. But I was blew me away. It blew that door open on my hand too, man. I just smack my hand. It says, and it's a customer. I don't know where I'm supposed to go. I'm not supposed to go in here. It's usually a person right here. Our service center. I don't know if this the place I'm supposed to be at, but I'm in here. Hello. I have a delivery for C McDonald. I think they're not supposed to come here. I'm not sure. It's the last name. It just says C. Okay. Thank you very much. You guys have a good day. Oh my God. It's okay. You have a great day. Enjoy. Oh, look at this joint. I ain't never seen this before. What is this? What is this joint? No. This is crazy nice. No. Okay. That's nice. You like it? How long have you had it? Three months? It's nice. I'm here to join with this order and get in order. Guess what, guys? You see this building right here? This big building. We sold this building like a couple years ago. It was a gun store. Now they're making it into a bakery for all the haters. That joint opens. Who am I going to go in that zone one day, baby? So we're here to join with this order. And I had to park in a parking lot because this joint, like right on the main street, I think 21 would be upstairs on this one. Guess 20. You got to go to 21. Maybe it's a big Reggie. Big Reggie, but we're 2170. You got me going to exercise? It has to be up here, doesn't it? Oh, yeah, there we go. That's 20. 21, baby. That's 19. 22. 20. There you go, boss. Be welcome. We have a good one. In just like that is the end of the video. I hope you guys enjoyed it, guys. And guess what, baby? We came and got a son. We didn't hit that 100 out of gold, but we did get our pockets on 12. And we didn't do that bad for two and a half hours. You guys are with me. Make sure you look at tomorrow. Let's go back down. Let's go back down. Because we ain't playing around. Why we ain't playing around? Because 2022 is still bad season. We don't give them a reason. I'll see you guys on the next one. We go, we go, we flow, you already know what it is, okay? And I'm on my way.


Everybody forest here ripping 2022 fanatics memorabilia silver box number 12 random hit style We got three hits in the fanatics silver box right here. I'll bring them all over and show them Sean's gonna type in as we go along do the random at the end to see who gets what good luck to the three of you in the break here Let's have some fun Alright, I don't know if we should start with it. Oh, it's pretty nice. All right, okay, okay Okay, this is a pretty good start I don't think this helmet is authentic. It's pretty light. I think it's a replica but Think we're more than we're gonna we're more than making up for it here with a player on the helmet Yeah, yeah replica Colts helmet Hayden Manning the sheriff's in town Hayden Manning signed Colts helmet. There you go camera easy Good luck on the Peyton. Everybody loves some Peyton. That's next. I already opened it It's uh It's a ball. It's a football That also this is the bag for that. I'm guessing. Yeah, so it's got a bag to football. You got the name You got it.  That is awesome, man That's sick. You know, all this is nothing to get by fanatics or stickers on all these somewhere.  That comes up with the attics bag as well. Yeah, one more It's a jersey. Oh, it's a hockey jersey. This is heavy It's got to be a hockey jersey. It's heavy hefty It is not Instead it's definitely authentic jersey though because it is heavy. It's Terry Bradshaw the original tb12 Terry Bradshaw Very nice. That's an authentic. Yeah, it says it on the sticker too beautiful Bradshaw Good luck on the random everybody tb12 All right, again, we'll roll the dice. We'll randomize the names and the hits same amount of time see who's getting what Four and one five times. Oh Peyton's on game day tomorrow, huh? I wonder who he's gonna pick No, he's not gonna pick Bama No All right names five times One two three four and five Brian g then john a then or w then five times on the hits One two three four and five The manningham at the Bradshaw jersey and then the uh trio signed football Did I not finish? Oh, I didn't finish this one Whoops final round five. Sorry or in w then john a then brian g. Sorry guys. I only did four on that one. That's my bad Sorry about that. So the order is correct now. Sorry for any confusion, but that is that is the correct one right there five and five So let me copy it over orange on then brian Sorry about that guys That is correct right there All right, orin gets the manning helmet john a gets the Bradshaw jersey and brian g gets the uh trio signed football Congrats on the hits. We'll get those results posted and we will uh Keep moving on


One thing that's very interesting about practitioners that end up in the alternative or non-traditional medical space is that many of us came into this with illnesses of our own, often chronic illnesses that were difficult to diagnose or difficult to treat. And many of us, because we've had these problems, had seen all of the traditional specialists.  Now in this video, I thought I would share some insights from my own story about how Chinese medicine really helped me and really saved me because it gave me tools that I could use to heal throughout my life. Hey, I'm Dr. Alex Hain, author of the health book, Master of the Day, Doctor of Chinese Medicine and licensed acupuncturist. So before we jump into this video, you guys, I have two very important links right below this video. The first is if you'd like to become a patient of mine locally in Los Angeles or virtually via telemedicine, there are contact info links to my private practice right below this video. And there's also a free guide for you for daily rituals that can potentially help you add years to your life with traditional Chinese medicine. So check those out right below this video. So these days, I often think about the path that I could have gone down. And the story that kind of encapsulated just how much my digestive problems had bothered me was this experience I had when I was dating this girl for a long time, a long-term serious relationship of mine. And we had been wanting to go to Thailand for a while. Now she was Filipino, so we had wanted to do Southeast Asia and the Philippines and Thailand. And while we were there, we had planned this really cool island hopping trip. Now what we were going to do is go to this famous beach that was in a Leonardo DiCaprio movie.  So this was kind of like the trip we were looking forward to for a long time, right? This was the trip that we had wanted to do for the entire year. I think for a lot of people, when they travel, they tend to get a little constipated, right? That's not that uncommon for the average person. But for me, because digestive problems were already my main problem all the time, no matter how healthy I ate. It had been four, five, six, it had been seven days since I had a bowel movement, right?  We weren't doing anything out of the ordinary. But for whatever reason, that's where I was. And so I was having such bad abdominal pain and such bad GI discomfort that I just was like, we had been planning this trip really for a whole year. And the island hopping was the main thing we wanted to do several days of just going through these beaches and these incredible viewpoints in Thailand. And here I am saying, I'm not getting on that boat, basically, unless I have a bowel movement. And because I hadn't eaten in two days, I was having abdominal pain and cramps. And here I am at this archetypal moment where for the first time in my life as a young person in his mid-20s, my health is now a barrier to me actually being able to live. And not just show up for a job that I hated, but to show up with a girl I was seriously interested in, in the dream honeymoon location for a dream experience. So that kind of encapsulated just how bad my problems were. And for someone that had never really abused himself with poor diet or lack of exercise or drugs or anything like that, I found it very unfair that I was suffering from all these problems all the time. And that was why one of the biggest concepts I talk about now in my videos is constitution. Because constitution represents some of the challenges your health predispositions you will have in your life. And that challenge, again, was not because of medical neglect or medical malpractice, like no one hurt me in medicine. I didn't take something that caused myself to be sick. I just had a tendency genetically to have those problems. So I'm stuck here on this archetypal weird experience where I'm like 25, about to have a dream experience with a girl that I loved. And here I am. Basically, I didn't go on this experience because of this digestive problem. That was one of the experiences that really convinced me that I need to go and exhaust every option medically, conventionally, and alternative to understand why this is happening in someone who otherwise eats healthy and takes good care of himself. What was going on? So when I went through this referral chain of, of practitioners started with the PCP to a dietitian referral to a GI specialist referral to etc, etc, etc. Right. And when I saw the GI specialist, he saw me for two minutes and said, you know, sounds like IBS, let's do a colonoscopy. Now, I said yes, because I was there and I didn't know any better. And a few days later, I just called and canceled it. And I was like, this makes no sense. There's not something seriously going wrong here. I just feel it. And there's got to be another way. And that was when I had this chance encounter sitting in a cafe one day, where I was just sitting down and literally at a notebook writing out every intervention I had tried documenting my results with every intervention. And then writing on another page before you exhaust all options, try these 10 15 things meditation, yoga, naturopath, acupuncture, whatever herbs and the universe kind of threw me a bone, because a guy walked in that day that I happened to know very loosely from working at the coffee shop. And he told me that he had developed autoimmune hepatitis as a rare reaction to a medication he'd been given for, I believe for Crohn's disease. And he said, you know, I saw this guy named Jacques was an acupuncturist. He's right around the corner like a stone's throw away behind this coffee shop. And you should go check him out. He didn't cure me, but he helped me a lot. He helped me more than anyone else I saw. So I see this French Chinese medicine doctor named Jacques. Now go figure. I had just moved back from China about two years before. And I'd gone to China to look for mystics and holy people and sages, etc. And go figure I never met a Chinese medicine mentor until I came back. And this guy Jacques lived less than one mile from my parents home in Connecticut. Talk about weird twist of fate in my healing hero's journey. So I go see this guy named Jacques. And Jacques gives me a basic herbal formula. He didn't even write custom. It was a pre made one. And the next 30 days, I had the most regular bowel function that I ever had in my entire life. 30 days. The next 30 day window was the most regular gi function my entire existence. And again, even though I wasn't cured, the problem wasn't gone for good. That just showed me at least symptomatically. There's something here that already works better than any conventional option I ever had. And no one no doctor ever referred me to this guy. No one ever told me Chinese medicine was a real thing that worked. No one ever hinted that this would be the next in the referral chain. And yet this just got me the best results I ever had. And this guy has a practice booked months out. So that really helped me understand that what is the standard of care is not always what is most clinically effective, even though that is how it's marketed, right? And that there are other options that are seriously effective, often more effective than whatever the standard of care is, you know, the Ivy League physician in the ivory tower with the white coat and all the placebo controlled blind studies. We put a lot of faith in that. And that's obviously gold standard research. And it is a course part of the marketing of traditional medical care to build authority and expertise. And it is valuable authority and expertise. But there's a lot more out there that works clinically. There's a lot more that doesn't work clinically too. But this whole series of experiences, this story, this arc, right, the healing heroes journey I call with my patients where there's the same heroes journey that Joseph Campbell talks about, but the descent into hell is the descent into illness. And often, the descent into hell is linked to specialists, specialists, specialists, doctor after doctor, and no one knows what's going on. And the person is still chronically ill to the point of not really being able to live the way that they normally can live. And I think about a lot about what would happen if I went down that journey, that path further, if I took that GI specialist advice that colonoscopy probably would have been negative, like they usually are with my patients. And then it would have been a medication referral probably to a psychiatrist for an SSRI and antidepressant.  And the way I'm confident of that is because I have so many patients that did not know that there were other alternative options that were clinically effective. And they go down that route and only come to me years later when there's a lot more work to be done. And no healing has been done. And it just makes me think that this journey, I see play out time after time after time after time after time again, the person who feels like they've exhausted every option that there is no more hope that honestly, they don't even want to try because they've tried every referral that somebody has given them. And every piece of advice that their friends and family give hurts them a little more because often their friends and family, they're ostracized a bit as if there's something wrong with them or it's in their head or it's not real or it's they're just too sensitive. But I shot this video, I think, not only to share a bit about my journey again, but to remind those people that there's probably nothing wrong with you and you're gut feeling about what's going on is probably freaking correct. And you should trust it and continue finding alternative advice, alternative counsel because someone out there has a solution for you and to not give up with all the things you've tried. Because I've been that jaded person, that was afraid I would never be normal again. And I thought I had exhausted every option. But the reason I'm in the field that I am, and the reason why I shoot these videos every week, no matter how tired I am, matter how busy I am, is because I know there's not just one, but probably millions of people out there that have gone through what I've gone through. And people who are harmed by medical interventions that they don't need and who don't know that there's something as clinically effective as what I practice. For me, this was the most effective thing I ever found. So that's what I'm dedicating my life to. But don't give up. There absolutely is an option somewhere someone has it. So if the traditional options aren't working for you, find a new one. Alright guys, fiery rant for today a little bit more about my story. Before you go, leave a comment right below this video. Let me know what it is in your journey that has made you want to give up, right? Comment there below. And don't forget, below the video, there is contact info for my private practice. You want to learn more, you can contact me right below this.