MLOps community meetup #49! Last Wednesday we talked to Lak Lakshmanan, Data Analytics and AI Solutions, Google Cloud. // Abstract: Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalogue tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Workflow Pipelines, Transform, Multimodal Input, Feature Store, Cascade) that are useful in the context of adding flexibility, resilience and reproducibility to ML in production. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects. Anyone designing infrastructure for machine learning will have to be able to provide easy ways for the data engineers, data scientists, and ML engineers to implement these, and other, design patterns. // Bio: Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. ----------- Connect With Us ✌️-------------    Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Lak on LinkedIn: https://www.linkedin.com/in/valliappalakshmanan/ Timestamps: [00:00] TWIML Con Debate announcement to be hosted by Demetrios on Friday [00:19] Should data scientists know about Kubernetes? Is it just one machine learning tool to rule them all? Or is it going to be the "best-in-class" tool? [00:35] Strong opinion of Lak about "Should data scientists know about Kubernetes?" [05:50] Lak's background into tech [08:07] Which ones you wrote in the book? Is the airport scenario yours? [09:25] Did you write ML Maturity Level from Google? [12:34] How do you know when to bring on perplexity for the sake of making things easier? [16:06] What are some of the best practices that you've seen being used in tooling?   [20:09] How did you come up with writing the book? [20:59] How did we decide that these are the patterns that we need to put in the book? [24:14] Why did I get the "audacity" to think that this is something that is worth doing? [31:29] What would be in your mind some of the hierarchy of design patterns? [38:05] Are there patterns out there that are yet to be discovered? How do you balance the exploitable vs the explorable ml patterns? [42:08] ModelOps vs MLOps [43:08] Do you feel that a DevOps engineer is better suited to make the transition into becoming a Machine Learning engineer? [46:07] Fundamental Machine Design Patterns vs Software Development Design Patterns [49:23] When you're working with the companies at Google, did you give them a toolchain and a better infrastructure or was there more to it? Did they have to rethink their corporate culture because DevOps is often mistaken as just a pure toolchain?

Coffee Sessions #28 with Charlie You of Workday, Lessons learned from hosting the Machine Learning Engineered podcast //Bio Charlie You is a Machine Learning Engineer at Workday and the host of ML Engineered, a long-form interview podcast aiming to help listeners bring AI out of the lab and into products that people love. He holds a B.S. in Computer Science from Rensselaer Polytechnic Institute and previously worked for AWS AI. Charlie is currently working as a Machine Learning Engineer at Workday. He hosts the ML Engineered podcast, learning from the best practitioners in the world.   Check Charlie's podcast and website here: mlengineered.com https://cyou.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Charlie on LinkedIn: https://linkedin.com/in/charlieyou/ Timestamps: [00:00] Introduction to Charlie You [01:50] Charlie's background on Machine Learning and inspiration to create a podcast [06:20] What's your experience been so far as the machine learning engineer and trying to put models into production and trying to get things out that has business value? [07:08] "I started the podcast because as I started working, I had the tingling that machine learning engineering is harder than most people thought, and like way harder than I personally thought." [08:20] What's an example of that where you target someone in your podcast, you keep that learning and you want an extra meeting the next day and say "Hey, actually I'm starting one of the world's experts on this topics and this is what they said"?    [10:06] In a world of tons of traditional software engineering assets and the process you put in place, how have they adopted what they're doing to the machine learning realm?    [19:00] About your podcast, what are some 2-3 most consistent trends that you've been seeing? [21:08] Instead of splintering so much as machine learning monitoring infrastructure specialist, are you going to departmentalize it in the future? [27:22] Is there such a thing as an MLOps engineer right now? [28:50] "We haven't seen a very vocal, very opinionated project manager in machine learning yet." - Todd Underwood [30:18] "Similarly with tooling, we haven't seen the emergence of the tools that encode those best practices." Charlie [31:42] "The day that you don't have to be a subject matter expert in machine learning to feel confident and deploy machine learning products, is the day that you will see the real product leadership in machine learning." Vishnu [34:12] I'd love to hear your take on some more trends that you've been seeing (Security and Ethics) [34:41] "Data Privacy and Security is always at the top of any consideration for infrastructure." Charlie [35:44] That's driven by legal requirements? How do you solve this problem? [37:27] How do we make sure that if that blows up, you're not left with nothing?   [42:28] In your conversations, have you seen people who goes with cloud provider? [43:25] Enterprises have much different incentives than startups do. [45:48] What are some used cases where companies are needing to service their entire needs? [45:48] What are some used cases where companies are needing to service their entire needs? [49:18] What are some takeaways that you had in terms of how you think about your career, what experiences you want to build as this MLOps based engineering is moving so fast?   [56:08] "Your edge is never in the algorithm"

Coffee Sessions #27 with Noah Gift of Pragmatic AI Labs, Practical MLOps  // A “Gift” from Above This week, Demetrios and Vishnu got to spend time with inimitable Noah Gift. Noah is a data science educator, who teaches at Duke, Northwestern, and many other universities, as well as a technical leader through his company Pragmatic AI Labs and past companies. His bio alone would take up this section of the newsletter, so we invite you to check it out here, as well as the rest of his educational content. Read on for some of our takeaways. // HOW is as important as WHAT In our conversation, Noah eloquently pointed out the numerous challenges of bringing ML into production, and especially for making sure it's used positively. It’s not enough to train great models; it’s important to make sure they impact the world positively as their productionized. How models are used is as important as what the model is. Noah specifically commented on externalities and how’s it incumbent on all MLOps practitioners to understand the externalities created by their models. // Just get certified As an educator, Noah has seen front and center how deficits in ML/DS education at the university level have led to the “cowboy” data scientist that doesn’t fit into an effective technical organizational structure. In his courses, Noah emphasizes getting started with off the shelf models and understanding how existing software systems are engineered before committing to building ML systems. Furthermore, Noah suggested getting certifications as a useful way of upskilling for anyone looking to increase their knowledge base in MLOps, especially by cloud providers. // Tech Stack Risk Finally, as many of you do, we debated the relative merits of the major cloud providers (AWS, Azure, and GCP) with Noah. With his vast experience, Noah made a great point about how adopting extremely new tools can sometimes go wrong. In the past, Noah adopted Erlang as a language used in the development of a product. However, as the language never quite took off (in his experience), it became a struggle to hire the right talent to get things done. Readers, as you go about designing and building the MLOps stack, does any part of the process sound like Noah’s experience with Erlang? Tools or frameworks where downstream adoption may end up fractured? We’d love to hear more! Definitely check out Noah’s podcast with us for more awesome nuggets on MLOps. Thanks to Noah for taking the time!  https://noahgift.com/ Noah Gift Machine Learning, Data Science, Cloud & AI Lecturer His most recent books are: Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018) Python for DevOps (O’Reilly, 2020).  Cloud Computing for Data Analysis, 2020 Practical MLOps (O'Reilly, 2021 est.) --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Noah on LinkedIn: https://www.linkedin.com/in/noahgift/ [00:00] Introduction to Noah Gift   [03:28] How we can stay pragmatic when it comes to MLOps? [32:45] The worst excuse that you can give somebody is that "I just do this stuff that's hard, intellectually, but departed, makes it work. That's your job."   [33:34] "In academics, we don't do vocational training, we just teach you theory." "In the Master's Degree, we don't do anything that gets you a job." [46:33] MLOps vs Cloud Provider [51:35] GO vs Erlang

MLOps community meetup #48! Last Wednesday, we talked to Manoj  Agarwal, Software Architect at Salesforce. // Abstract: Serving machine learning models is a scalability challenge at many companies. Most applications require a small number of machine learning models (often < 100) to serve predictions. On the other hand, cloud platforms that support model serving, though they support hundreds of thousands of models, provision separate hardware for different customers. Salesforce has a unique challenge that only very few companies deal with; Salesforce needs to run hundreds of thousands of models sharing the underlying infrastructure for multiple tenants for cost-effectiveness. // Takeaways: This talk explains Salesforce hosts hundreds of thousands of models on a multi-tenant infrastructure to support low-latency predictions. // Bio: Manoj Agarwal is a Software Architect in the Einstein Platform team at Salesforce. Salesforce Einstein was released back in 2016, integrated with all the major Salesforce clouds. Fast forward to today and Einstein is delivering 80+ billion predictions across Sales, Service, Marketing & Commerce Clouds per day. //Relevant Links https://engineering.salesforce.com/flow-scheduling-for-the-einstein-ml-platform-b11ec4f74f97 https://engineering.salesforce.com/ml-lake-building-salesforces-data-platform-for-machine-learning-228c30e21f16 ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Manoj on LinkedIn: https://www.linkedin.com/in/agarwalmk/ Timestamps: [00:00] Happy birthday Manoj! [00:41] Salesforce blog post about Einstein and ML Infrastructure [02:55] Intro to Serving Large Number of Models with Low Latency [03:34] Manoj' background [04:22] Machine Learning Engineering: 99% engineering + 1% machine learning - Alexey Gregorev on Twitter [04:37] Salesforce Einstein [06:42] Machine Learning: Big Picture [07:05] Feature Engineering [07:30] Model Training [08:53] Model Serving Requirements [13:01] Do you standardize on how models are packaged in order to be served and if so, what standards Salesforce require and enforce from model packaging? [14:29] Support Multiple Frameworks   [16:16] Is it easy to just throw a software library in there? [27:06] Along with that metadata, can you breakdown how that goes?   [28:27] Low Latency [32:30] Model Sharding with Replication [33:58] What would you do to speed up transformation code run before scoring? [35:55] Model Serving Scaling [37:06] Noisy Neighbor: Shuffle Sharding [39:29] If all the Salesforce Models can be categorized into different model type, based on what they provide, what would be some of the big categories be and what's the biggest? [46:27] Retraining of the Model: Does that deal with your team or is that distributed out and your team deals mainly this kind of engineering and then another team deal with more machine learning concepts of it? [50:13] How do you ensure different models created by different teams for data scientists expose the same data in order to be analyzed? [52:08] Are you using Kubernetes or is it another registration engine? [53:03] How is it ensured that different models expose the same information?

**Private data, Data Science friendly** Data Scientists are always eager to get their hands on more data, in particular, if that data has any value that can be extracted. Nevertheless, in real-world situations, data does not exist in the abundance that we thought existed, in other situations, the data might exist, but not possible to share it with different entities due to privacy concerns, which makes the work of data scientists not only hard, but sometimes even impossible. // Abstract: In the last episode of this series, we've decided to bring not one, but two guests to tells us how Synthetic data can unlock the use of data for Data Science teams whenever privacy concerns are a reality.  Jean-François Rajotte, Researcher and Resident data Scientist at the University of Columbia and Sumit Mukherjee, Senior Applied Scientist at Microsoft's AI for Good, bring us into more detail their expertise not only, in Synthetic data generation, but in it's mind blowing combination with Federated Learning to take the healthcare sector into the next level of AI adoption. //Other links to check on Jean-François Rajotte: https://venturebeat.com/2021/01/20/microsofts-felicia-taps-ai-to-enable-health-providers-to-share-data-anonymously/ https://dsi.ubc.ca/ https://leap-project.github.io/ //Other links to check on Sumit Mukherjee: www.sumitmukherjee.com (Sumit research) https://arxiv.org/abs/2101.07235 https://arxiv.org/pdf/2009.05683.pdf https://github.com/microsoft/privGAN (PrivGan) //Final thoughts Feel free to drop some questions into our slack channel (https://go.mlops.community/slack)  Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ ----------- Connect With Us ✌️------------- Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Fabiana on LinkedIn: https://www.linkedin.com/in/fabiana-clemente/ Connect with Jean-François on LinkedIn: https://www.linkedin.com/in/jfraj/ Connect with Sumit on LinkedIn: https://www.linkedin.com/in/sumitmukherjee2/

Coffee Sessions #26 with Vishnu Rachakonda of Tesseract Health, Daniel Galinkin of iFood, Matias Dominguez of Rappi & Simarpal Khaira of Intuit, Feature Store Master Class. //Bio Vishnu Rachakonda Machine Learning Engineer at Tesseract Health. Coffee sessions co-host but this time his role is one of the all-stars guest speakers. Daniel Galinkin One of the co-founders of Hekima, one of the first companies in Brazil to work with big data and data science, with over 10 years of experience in the field. At Hekima, Daniel was amongst the people responsible for dealing with infrastructure and scalability challenges. After iFood acquired Hekima, he became the ML Platform Tech Lead for iFood. Matias Dominguez   A 29-year-old living in Buenos Aires, past 4.5 years working on fraud prevention.  Previously at MercadoLibre and other random smaller consulting shops. Simarpal Khaira Simarpal is the product manager driving product strategy for Feature Management and Machine Learning tools at Intuit. Prior to Intuit, he was at Ayasdi, a machine learning startup, leading product efforts for machine learning solutions in the financial services space. Before that, he worked at Adobe as a product manager for Audience Manager, a data management platform for digital marketing. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Daniel on LinkedIn: https://www.linkedin.com/in/danielgalinkin/ Connect with Matias on LinkedIn: https://www.linkedin.com/in/mndominguez/ Connect with Simarpal on LinkedIn: https://www.linkedin.com/in/simarpal-khaira-6318959/  Timestamps: [00:00] Introduction to guest speakers. [00:33] Vishnu Rachakonda Background [01:00] Guest speakers' Background [03:13] Are Feature Stores for everyone? [04:02] Guest speakers' Feature Store background [17:09] How do you go about gathering requirements for a Feature Store and customize it? [17:34] Guest speakers' process for Feature Store [31:14] What solution are we actually trying to build? [36:42] How do you ensure consistency in your transformation logic and in your process generating features? [43:39] In terms of versioning that transformation logic and knowledge that goes into creating Feature Stores and allowing them to be reusable and consistent, how are you going to grapple with that? [48:06] How do you bake in best practices into the services that you offer? [49:34] "It's too possible for you to do something wrong. You have to specify that wrong thing. That makes it harder to do that wrong thing." Daniel [51:54] "It starts with changing the mindset. Making people getting the habit of what is the value here. Then you are producing features for consumers because tomorrow you could become a consumer. Write it in a way as you want to consume somebody's feature." Simar [56:51] "As part of that process, it should come with everyone's best practices to actually improve all features" Matias

MLOps community meetup #47! Last Wednesday, we talked to Adrià Romero, Founder and Instructor at ProductizeML. // Abstract: In this talk, we tackled:   - Motivations and mission behind ProductizeML. - Common friction points and miscommunication between technical and management/product teams, and how to bridge these gaps.   - How to define ML product roadmaps, (and more importantly, how to get it signed off by all your team). - Best practices when managing the end-to-end ML lifecycle. / Takeaways: - Self-study guide that reviews the end-to-end ML lifecycle starting with some ML theory, data access and management, MLOps, and how to wrap up all these pieces in a viable but still lovable product.   - Free and collaborative self-study guide built by professionals with experience on different stages from the ML lifecycle. // Bio: Adrià is an AI, ML, and product enthusiast with more than 4 years of professional experience on his mission to empower society with data and AI-driven solutions. Born and raised in the beautiful and sunny Barcelona, he began his journey in the AI field as an applied researcher at the Florida Atlantic University, where he published some of the first deep learning works in the healthcare sector. Attracted by the idea of deploying these ideas to the real world, he then joined Triage, a healthcare startup building healthcare solutions powered by AI, such a smartphone app able to detect over 500 skin diseases from a picture. During this time, he has given multiple talks at conferences, hospitals, and institutions such as Novartis and Google. Previously, he interned at Huawei, Schneider Electric, and Insight Center for Data Analytics. Early this year, he started crafting ProductizeML, An Instruction and Interactive Guide for Teams Building Machine Learning Products where he and a team of AI & product specialists carefully prepare content to assist on the end-to-end ML lifecycle. ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register   Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Adria on LinkedIn: https://www.linkedin.com/in/adriaromero/ References mentioned on this episode: https://en.wikipedia.org/wiki/ImageNet https://twitter.com/productizeML https://course.productize.ml/ https://github.com/ProductizeML/gitbook https://adria756514.typeform.com/to/V4BDqjYA - Newsletter Signup https://www.buymeacoffee.com/ Timestamps: [00:00] Introduction to Adrià Romero   [00:32] How did you get into tech? [02:16] ImagiNet Project (Visual Recognition Challenge) [06:49] Visual Recognition with Skin Lesions [07:05] Fundamental vs Applied Research (Academia experience) [08:44] Motivation for technology [14:55] Transition to ProductizeML [19:09] ProductizeML Context [23:50] What was its that made you think that Education is probably more powerful? [24:21] ProductizeML Objective [26:55] Ethics: Do you want to put that in there later? [30:12] ProductizeML Content Format and Tools [34:07] ProductizeML Catalogue [39:28] ProductizeML Audience Target [42:54] "Buy me a coffee" platform [48:29] Do you ever foresee with the educational being more vertical-specific?

The revolution of Federated Learning - And we're back with another episode of the podcast When Machine Learning meets Privacy! For the episode #8 we've invited Ramen Dutta, a member of our community and founder of TensoAI. // Abstract: In this episode,  Ramen explain us the concept behind Federated Learning, all the amazing benefits and it's applications in different industries, particularly in agriculture. It's all about not centralizing the data, sound awkward? Just listen to the episode. //Other links to check on Ramen: https://www.linkedin.com/in/tensoai/ https://www.tensoai.com/ https://twitter.com/tensoAI //Final thoughts Feel free to drop some questions into our slack channel (https://go.mlops.community/slack)  Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ ----------- Connect With Us ✌️------------- Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Fabiana on LinkedIn: https://www.linkedin.com/in/fabiana-clemente/ Connect with Ramen on LinkedIn: https://www.linkedin.com/in/tensoai/

Coffee Sessions #25 with Marian Ignev of CloudStrap.io & SashiDo.io, Most Underrated MLOps Topics. //Bio Marian a passionate entrepreneur, backend dude & visionary. These are the three main things described to Marian very well: Marian's everyday routines include making things happen and motivating people to work hard and learn all the time because I think success is a marathon, not just a sprint! Marian loves to communicate with bright creative minds who want to change things.   His favorite professional topics are backend stuff, ops, infra, IoT, AI, Startups, Entrepreneurship.   In his free time, he loves to cook for my family. --------------- ✌️Connect With Us ✌️ -------------  Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Marian on LinkedIn: https://www.linkedin.com/in/mignev/ Timestamps: [00:00] Introduction to Marian Ignev [01:57] Marian's background [10:06] Who do you need and what should they be doing? [18:05] How are you solving problems at your company? [27:22] What are your thoughts around ML tooling? Why that hasn't happened yet and why it will change? [33:16] You can't actually figure out what's the main focus of ML tooling services.   [34:14] "Start small and start simple to focus only on a small problem that you can bring more than the others." [37:14] How are you making the case for how standardization to occur in your initial MLOps? [38:08] "If you're doing a mistake somewhere, do it everywhere because it will be very easy to find and replace it after that."    [41:50] How do you model monitoring? [47:19] How would you recommend people to get started? [49:00] Ecosystem of Machine Learning in Eastern Europe Other links you can check on Marian: https://www.sashido.io/ https://www.cloudstrap.io/ https://twitter.com/mignevm.ignev.net/blog/  (Blog) m.ignev.net/  (Personal Website) fridaycode.net/  (FridayCode)

MLOps community meetup #46! Last Wednesday, we talked to Hendrik Brackmann, Director of Data Science and Analytics at Tide. // Abstract: Tide is a U.K.-based FinTech startup with offices in London, Sofia, and Hyderabad. It is one of the first, and the largest business banking platform in the UK, with over 150,000 SME members. As of 2019, one of Tide’s main focuses is to be data-driven. This resulted in the forming of a Data Science and Analytics Team with Hendrik Brackmann at its head. Let's witness Hendrik's personal anecdotes in this episode! // Bio: After studying probability theory at the University of Oxford, Hendrik joined MarketFinance, an SME lender, in order to develop their risk models. Following multiple years of learning, he joined Finiata, a Polish and German lender in order to build out their data science function. Not only did he succeed in improving the risk metrics of the company he also learnt to manage a different department as interim Head of Marketing. Hendrik's job as Director of Data Science and Analytics at business bank Tide is to oversee data engineering, data science, insights and analytics and data governance functions of Tide. // Final thoughts Please feel free to drop some questions you may have beforehand into our slack channel (https://go.mlops.community/slack) Watch some old meetups on our youtube channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Hendrik on LinkedIn: https://www.linkedin.com/in/hendrik-brackmann-b2b5477a/ Timestamps: [00:00] Introduction to Hendrik Brackmann [01:54] Hendrik's background into tech [03:22] First Phase of the three epic journeys of Hendrik [08:05] Were there some hiccups you were running into as you're trying to make things better?   [10:50] Any other learnings that you got from that job that you want to pass along to us? [11:50] You were doing all batch at that point, right? [12:35] Phase 2: of Hendrik's epic journey [15:11] Did you eventually cut down at the time that it took? [15:50] Breakdown of Transformation terminologies and its importance [19:03] What are some things that you would never do again? [20:32] How did you see things more clearly? [22:30] Phase 3: Moving on to Tide [24:46] Have you only worked with teams with one programming language? [30:47] Did you try to open-source solutions or did you just go right out to buy it? [33:12] What is real-time for you? How much latency is there? How much time do you need? [37:18] What stage did you realize to get the feature store?   [40:09] What would you recommend from a maturity standpoint to get a feature store? [41:20] Can you summarize some of the greatest problems that the feature stores solve for you?    [42:22] What problems does a feature store introduces if any? [44:39] Where do the model and the feature start from the perspective of a system in the engineering? [49:15] You need a good data management in feature stores [50:21] Have you ever used or built any feature stores that explicitly handle units and does dimensional analysis on derived features? [54:46] What kind of models do you have up at the moment and how do you test and monitor and deploy the models?