MLOps community meetup #56! Last Wednesday we talked to  Daniel Stahl, Head of Data and Analytic Platforms, Regions Bank. // Abstract: The Data Science practice has evolved significantly at Regions, with a corresponding need to scale and operationalize machine learning models. Additionally, highly regulated industries such as finance require a heightened focus on reproducibility, documentation, and model controls.  In this session with Daniel Stahl, we will discuss how the Regions team designed and scaled their data science platform using DevOps and MLOps practices.  This has allowed Regions to meet the increased demand for machine learning while embedding controls throughout the model lifecycle.  In the 2 years since the data science platform has been onboarded, 100% of data products have been successfully operationalized. // Bio: Daniel Stahl leads the ML platform team at Regions Bank and is responsible for tooling, data engineering, and process development to make operationalizing models easy, safe, and compliant for Data Scientists.   Daniel has spent his career in financial services and has developed novel methods for computing tail risk in both credit risk and operational risk, resulting in peer-reviewed publications in the Journal of Credit Risk and the Journal of Operational Risk. Daniel has a Masters in Mathematical Finance from the University of North Carolina Charlotte.      Daniel lives in Birmingham, Alabama with his wife and two daughters. ----------- 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 Dan on LinkedIn: https://www.linkedin.com/in/daniel-stahl-6685a52a/ Timestamps: [00:00] Introduction to Ben Wilson [00:11] Ben's background in tech [01:17] "How do you do what I have always done pretty well which is being as lazy as possible in order to automate things that I hate doing. So I learned about Regression Problems." [03:40] Human aspect of Machine Learning in MLOps [05:51] MLOps is an organizational problem [09:27] Fragile Models [12:36] Fraud Cases [15:21] Data Monitoring [18:37] Importance of knowing what to monitor for [22:00] Monitoring for outliers [24:16] Staying out of Alert Hell [29:40] Ground Truth [31:25] Model vs Data Drift on Ground Truth Unavailability [34:25] Benefit to monitor system or business level metrics [38:20] Experiment in the beginning, not at the end [40:30] Adaptive windowing [42:22] Bridge the gap [46:42] What scarred you really bad?

MLOps community meetup #55! Last Wednesday we talked to Igor Lushchyk, Data Engineer, Adyen.   // Abstract: Building Data Science and Machine Learning platforms at a scale-up. Having the main difficulty in finding correct processes and basically being a toddler who learns how to walk on a steep staircase. The transition from homegrown platform to open source solutions, supporting old solutions and maturing them with making data scientists happy.   // Bio: Igor is a software engineer with more than 10 years of experience. With a background in bioinformatics, he even started PhD but didn't finish it. As a data engineer, Igor has been working for the last 6 or 7 years, or maybe more - because he was doing almost the same data engineering stuff but his position was named differently. Igor has been doing a lot of MLOps in 4-5 years now. He doesn't know what he was doing more then - Data Engineering or MLOps. And that’s how this topic came about.   ----------- 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 Igor on LinkedIn: https://www.linkedin.com/in/igor-lushchyk/ Timestamps: [00:00] Introduction to Igor Lushchyk [02:05] Igor's background in tech [07:42] Tips you can pass on [11:05] How these tools work and how they play together and what is underneath? [13:18] Dedicated MLOps team [13:55] Central Data Infrastructure Section [16:57] Transfer over to open-source [20:24] If you don't plan for production from the beginning, then it's going to be painful trying to go from POC to production. [22:08] Ho do you handle data lineage? [25:09] You chose that back in the day but you're regretting it. [26:34] "Try to use tools which solve 80% of your use cases and maybe 20% you'll have the suffering but at least it's not 100% suffering." [27:27] Friction points [28:53] Interaction with Data Scientists [29:21] "We have alignment sessions. We have different levels of representations. We share our progress." [32:42] Build verse by decisions [34:04] When to build or grab an open-source tool [35:51] Build your own or buy open-source? [37:11] Certain maturity and a certain number of engineers [38:11] Startup to go with open-source [40:14] Correct transition process [40:56] "There are no other ways but to communicate with data scientists. Your team needs to have a close loop for future priorities, what to take with you and what to leave behind." [44:51] What to use in monitoring piece [45:36] Prometheus and Grafana [48:07] Do you automatic retriggering monitoring of Models set up? [51:55] Hardware for on Prim model training [52:38] "Machine Learning model prediction is a spear bomb." [53:55] War or horror stories [54:15] "Guys, don't do context switching!" [55:54] "I won't say that Adyen is a company that allows you to make mistakes but you can make mistakes."

MLOps community meetup #54! Last Wednesday we talked to Laszlo Sragner, Founder, Hypergolic. // Abstract: How my experience in quant finance and software engineering influenced how we ran ML at a London Fintech Startup. How to solve business problems with incremental ML? What's the difference between academic and industrial ML? // Bio: Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, he drove the company's data strategy delivering solutions to Tier 1 investment banks and hedge funds. He currently runs Hypergolic (hypergolic.co.uk) an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations. // Takeaways Continuous evaluation and monitoring is indistinguishable in a well set up product team. Separation of concerns (SE, ML, DevOps, MLOps) is very important for smooth operation, low friction team coordination/communication is key. To be able to iterate business features into models you need a modelling framework that can express these which is usually a DL package. DS-es are well motivated to go more technical because they see the rewards of it. All well run (from DS perspective) startups in my experience do the same. // Other Links Free eBook about MLPM: https://machinelearningproductmanual.com/ Lightweight MLOps Python package: https://hypergol.ml/ Blog: laszlo.substack.com ----------- 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 Laszlo on LinkedIn: https://www.linkedin.com/in/laszlosragner/ Timestamps: [00:00] Introduction to Laszlo Sranger [02:15] Laszlo's Background [09:18] Being a Quant, then influenced, what you were doing with the Investment Banks? [12:24] Do you think this can be applied in different use cases or specific to what you are doing? [14:41] Do you have any thoughts of a potentially highly opinionated person? [16:54] Product management in Machine Learning [24:59] You have to be at a large company or you have to have a large team? [26:38] What are your thoughts on MLOps products helping with product management for ML? Is it an overreach or scope creep? [32:00] In the messy world of startups due to the big cost of an MVP for NLP is RegEx which means to user feedbacks it's incorporated by tweaking RegEx? [33:04] Does the ensemble recent models more than older models? If so, what is the decay rate of weights for older models? [35:40] Since the iterative management model is generic enough for most ML projects, which component of it can be easily generalized and tools built for version control? [36:38] Topic Extraction: What type of model do you train for that task? [52:55] Thoughts on Notebooks [53:34] "I don't hate notebooks. Let's be clear about that. I put it this way, notebooks are whiteboards. You don't want your whiteboards to be your output because it's a sketch of your solution. You want the purest solution."

This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3.   // Diagram Link:   https://github.com/dmangonakis/mlops-lab-example-yelp   --------------- ✌️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 Laszlo on LinkedIn https://www.linkedin.com/in/laszlosragner/ Connect with Artem on LinkedIn: https://www.linkedin.com/in/artem-yushkovsky/ Connect with Paulo on LinkedIn: https://www.linkedin.com/in/paulo-maia-410874119/ Connect with Dimi on LinkedIn:

MLOps community meetup #53! Last Wednesday we talked to Krishna Gade, CEO & Co-Founder, Fiddler AI. // Abstract: Training and deploying ML models have become relatively fast and cheap, but with the rise of ML use cases, more companies and practitioners face the challenge of building “Responsible AI.” One of the barriers they encounter is increasing transparency across the entire AI lifecycle to not only better understand predictions, but also to find problem drivers. In this session with Krishna Gade, we will discuss how to build AI responsibly, share examples from real-world scenarios and AI leaders across industries, and show how Explainable AI is becoming critical to building Responsible AI. // Bio: Krishna is the co-founder and CEO of Fiddler, an Explainable AI Monitoring company that helps address problems regarding bias, fairness and transparency in AI. Prior to founding Fiddler, Gade led the team that built Facebook’s explainability feature ‘Why am I seeing this?’. He’s an entrepreneur with a technical background with experience creating scalable platforms and expertise in converting data into intelligence. Having held senior engineering leadership roles at Facebook, Pinterest, Twitter, and Microsoft, he’s seen the effects that bias has on AI and machine learning decision-making processes, and with Fiddler, his goal is to enable enterprises across the globe solve this problem. ----------- 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 Krishna on LinkedIn: https://www.linkedin.com/in/krishnagade/ Timestamps: [00:00] Thank you Fiddler AI! [01:04] Introduction to Krishna Gade [03:19] Krisha's Background [08:33] Everything was fine when you were doing it behind the scenes. But then when you put it out into the wild, we just lost our "baby." It's no longer under our control. [08:53] "You want to have the assurance of how the system works. Even if it's working fine or if it's not working fine."   [09:37] What else is Explainability? Can you break that down for us? [13:58] "Explainability becomes the cornerstone technology to have in place for you to build Responsible AI in production." [14:48] For those used cases that aren't as high stakes, do you feel it's important? Is it up the foodchain? [18:47] Can we dig into that used case real fast? [22:01] If it is a human doing it, there's a lot more room for error? Bias or theories can be introduced and then they don't have a basis in reality? [23:51] Do you need these subject matter experts or someone who is very advanced to be able to set up what the Explainability tool should be looking for at first is it that plug and play and it will know it latches on to the model? [29:36] Does Explainable AI also entail Explainable Data. I see the point where Explainability can help with getting the insights about data after the model has been trained but should it be handled perhaps more proactively where you unbias the data before training the model on it? [32:16] As a data scientist, there are situations when the prediction output is expected to support a business decision taken by senior executives. In that case, when the Explainable model gives out a prediction that doesn't align with the stakeholder's expectations, how should one navigate through this tricky situation? [43:49] How are denen gram clustering for data explainability?

This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 1. // Diagram Link: https://github.com/mlops-labs-team1/engineering.labs#workflow --------------- ✌️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 Alexey on LinkedIn: https://www.linkedin.com/in/alexeynaiden/ Connect with John on LinkedIn: https://www.linkedin.com/in/johnsavageireland/ Connect with Michel on LinkedIn: https://www.linkedin.com/in/michel-vasconcelos-8273008/ Connect with Varuna on LinkedIn: https://www.linkedin.com/in/vpjayasiri/ Timestamps [00:00] Introduction to Engineering Labs Participants [00:34] What are the Engineering Labs? [01:05] Credits to Ivan Nardini who organized this episode!   [04:24] John Savage Profile [05:13] Did you want to learn MLFlow before this? [05:50] Alexey Naiden Profile   [07:26] Varuna Jayasiri Profile [08:28] Michel Vasconcelos Profile [10:07] Do something with Pytorch and MLFlow and then figure out the rest: What did the process look like for you all?  What have you created? [13:39] What did the implementation look like? How you went about structuring and coding it? [17:03] Did you encounter problems along the way? [20:26] Can you give us a rough overview of what you designed and then where was the first problem you saw? [23:08] Was there a lot to catch up with or did you feel it was fine. Can you explain how it was? [24:12] Talk to us about this tool that you have that John was calling out. What was it called? [24:41] Is this homegrown? You built this? [24:51] Did you guys implement this when you went to the engineering labs? [26:03] Can you take us through the pipeline and then the serving and what the overall view of the diagram is? [37:26] For a pet project it works well, but when you wanna start adding a little bit more on top of it wasn't doing the trick? [38:13] So you see it coming in it's much less of an integral part, another lego building block that is part of the whole thing? [40:54] Did you all have trouble with Pytorch or MLFlow? [42:44] Along with that, what was the prompt you were encountering when you were trying to use Torchserve? [44:27] What are you thinking would have been better in that case? [49:05] Feedback on how Engineering Labs went [50:20] Michel: "Engineering Labs should go on. I would like to be a part of it in the next lab." [51:52] Varuna: "This gives me a tangible thing to look at at any point in time and learn from it." [53:00] John: "I feel I have an anchor into the world of MLOps from having done this lab." [55:52] Alexey: "We're at a checkpoint where there are ways we could take" [56:01] Terraform piece Michel wrote for reproducibility.

MLOps community meetup #52! Last Wednesday we talked to Luke Feeney and Gavin Mendel-Gleason, TerminusDB. // Abstract: A look at the open-source 'Git for Data' landscape with a focus on how the various tools fit into the pipeline. Following that scene-setting, we will delve into how and why TerminusDB builds a revision control database from the ground up. // Takeaways - Understanding the 'git for data' offering and landscape - See how to technically approach a revision control database implementation - Dream of a better tomorrow // Bio: Luke Feeney Operations Lead, TerminusDB   Luke Feeney is Operations Director at TerminusDB. Prior to joining TerminusDB, Luke worked in the Irish Foreign Ministry for a number of years. He served in Ireland’s Permanent Mission to the UN in New York and the Embassies in South Africa and Greece. He was Ireland’s acting Ambassador to Greece for 2016 and 2017. Luke was also the Head of the Government of Ireland’s Brexit Communications Team and the Government Brexit Spokesperson from 2017 to 2018. Gavin Mendel-Gleason Chief Technology Officer, TerminusDB   Dr Gavin Mendel-Gleason is CTO of TerminusDB. He is a former research fellow at Trinity College Dublin in the School of Statistics and Computer Science. His research focuses on databases, logic and verification in software engineering. His work includes contributing to the Seshat global historical databank, an ambitious project to record and analyse patterns in human history. He is the inventor of the Web Object Query Language and the primary architect of TerminusDB. He is interested in improving the best practices of the software development community and a strong believer in formal methods and the use of mathematics and logic as disciplines to increase the quality and robustness of software. ----------- 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 Luke on LinkedIn: https://www.linkedin.com/in/luke-feeney/ Connect with Gavin on LinkedIn: https://www.linkedin.com/in/gavinmendelgleason/ Timestamps: [00:00] MLOps Announcements [00:17] Slack Community [00:59] Luke and Gavin's Presentation Style [01:34] MLOps Community Twitter, LinkedIn and Youtube [01:45] Introduction to Luke Feeney and Gavin Mendel-Gleason [04:35] Luke: You wanted Git for Data? [05:17] Deep Breath || Is there a Git for Data? [06:30] What is Git for Data? [08:55] Four Big Buckets [28:43] Jupiter Notebook [30:20] Gavin: Collaboration for Structured Data [31:28] What about gitdifs with gitlfs? [31:40] Outline: Motivation, Challenges, Solution [35:35] Motivation: Why Structured Data? [36:08] Data is Core [37:34] Challenges: Data is Still in the Dark Ages [37:40] Structured or Unstructured, we're doing it wrong [40:15] Managing Data means Collaborating [45:09] Discoverability and Schema: Structured data requires a real database - not just GIT. [46:27] Revision Control [47:00] Collaboration [48:38] "Git for data, data is the new oil." [49:01] Why merging is so difficult? [49:25] "If you have a schema, you can do much more intelligent things." [52:36] Machine Learning and Revision Control

MLOps community meetup #51! Last Wednesday we talked to Pamela Jasper, AI Ethicist, Founder, Jasper Consulting Inc. // Abstract: One of the challenges to the widespread adoption of AI Ethics is not only its integration with MLOps, but the added processes to embed ethical principles will slow and impede Innovation. I will discuss ways in which DS and ML teams can adopt Agile practices for Responsible AI. // Bio: Pamela M. Jasper, PMP is a global financial services technology leader with over 30 years of experience developing front-office capital markets trading and quantitative risk management systems for investment banks and exchanges in NY, Tokyo, London, and Frankfurt. Pamela developed a proprietary Credit Derivative trading system for Deutsche Bank and a quantitative market risk VaR system for Nomura. Pamela is the CEO of Jasper Consulting Inc, a consulting firm through which she provides advisory and audit services for AI Ethics governance. Based on her experience as a software developer, auditor and model risk program manager, Pamela created an AI Ethics governance framework called FAIR – Framework for AI Risk which was presented at the NeurIPS 2020 AI conference. Pamela is available as an Advisor, Auditor and Keynote Speaker on AI Ethics Governance. She is a member of BlackInAI, The Professional Risk Managers Industry Association, Global Association of Risk Managers and ForHumanity. //Takeaways Agile methods of adopting AI Ethical processes. ----------- 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 Pamela on LinkedIn: https://www.linkedin.com/in/pamela-michelle-j-a5a3a914/ Timestamps: [00:00] Introduction to Pamela Jasper [00:17] Pamela's Background [05:45] Agile IA/Agile Machine Learning, If they are the right fit for each other? [07:50] What is agile? Not necessarily in and of itself a hard-coded framework. [08:05] Agile itself based on May 2001 Manifesto is simply a set of values and principles and teams that make decisions around these values and principles. [10:17] Proposal of Pamela: Let's do Agile with the underlying Ethics that are involved in the ways that you're creating this machine learning. Is that correct? [10:28] "What I'm suggesting is that Ethics become baked into almost to the mindset of a machine learning engineer, data scientists and in the machine learning operational process for MLOps." [14:37] "Not all models are created equal" [15:59] How would be in an Agile way put into practice in your mind? [36:38] What are the things that would help bridge the gap between AI Ethics and the Agile? [41:01] It's not that you're trying to bring on the Agile framework to the different pieces of Ethics. It's that you're bringing that into the Agile framework? [41:21] "We're weaving Ethics into the bedrock of existing Machine Learning practices." [45:13] How can you really get a diverse team if you're not hiring someone who's there as a diverse person? [48:59] What would Epics look like if you're baking Ethics? [52:52] How do you apply Ethics to an ethically questionable domain like gambling? [54:42] "I think that we can create an AI app for gambling is legal that becomes legal in that construct." [56:23] Do you think it's possible/desirable to automate any of the ethical considerations in this way?

Coffee Sessions #29 with Jet Basrawi of Satalia, Culture and Architecture in MLOps. //Bio Jet started his career in technology as a game designer but became interested in programming. He found he loved it. It was endlessly challenging and deeply enjoyable "Flow" activity. It was also nice to be in demand and earn a living. In the last several years, Jet been passionate about DevOps as a key strategic practice. About a year ago, he came into the AI world and it is a great place to be for someone like him. The challenges of MLOps and all the things surrounding AI delivery is a great space to work in. At about the time Jet got into AI the MLops community began, and it was a great experience to come on the journey with Demetrios who was uncovering topics in parallel to him. It was uncanny that each week Demetrios would run a meetup that dealt with exactly the topics he has been trying to reason about. Jet is very interested in culture and architecture and looking forward to exploring this subject in conversation. //Takeaways Insight into the role of culture and architecture in MLOps. --------------- ✌️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 Jet on LinkedIn: https://www.linkedin.com/in/jet-basrawi-4b9ab43/ Timestamps: [00:00] Introduction to Jet Basrawi [01:24] Jet's take on MLOps [02:00] "MLOps - the real Kung fu's in the future" Jet [02:35] Jet's different opinion on "Tooling is the biggest piece in MLOps".   [04:23] MLOps is a way of life. It's a lifestyle. It's not just tooling. [04:38] Why do you have to move over to the cultural side and where feel things fail culturally when it comes to machine learning? [05:47] What you refer to as an orthodox perspective on DevOps and how that place out in your perspective on MLOps?   [06:37] Why do you believe that the separate terminology is coming about and do you believe that this is ultimately harmful to organizations to have this confusion or do you think things should be simplified? [09:05] As soon as you go down and you're not looking at the big picture. You go down one level and they divert completely, is that your thought too? [12:30] How do you go about educating yourself and then figuring out how to articulate MLOps or constitutes in your organization? [16:16] How to do things differently? What are some of your preferred tactics? How to encourage culture change?   [19:02] "Management is NOT Leadership" [20:13] Why are people stuck in their agile approach? [23:57] Someone's trying to pick something up for the 1st time and then put it into production, how dangerous that can be? [25:53] Accepting failure [29:11] What are some of your principles that helped you communicate to the developers? [35:33] "It has to dumb down." [37:43] Annotation [39:37] "Patterntastic" [41:24] "MLOps is a people problem." [43:50] Sprint are adequate for machine learning? [47:03] "Software development is a social activity" [48:03] "We are all juniors in this field." //Show Notes https://www.youtube.com/watch?v=J1WpAJRt3rg Charlie You https://youtu.be/J36xHc05z-M Manoj https://www.youtube.com/watch?v=vH7UFZZdja8&t=5s Lak design patterns https://www.youtube.com/watch?v=9g4deV1uNZo&t=1s flavios talk https://continuousdelivery.com/implementing/culture/ westrum culture   https://www.youtube.com/watch?v=Y4H8dW7Ium8&feature=youtu.be&t=109 Jez Humble

MLOps community meetup #50! Last Wednesday we talked to Michael Del Balso, Willem Pienaar and David Aronchick, // Abstract: The MLOps tooling landscape is confusing. There’s a complicated patchwork of products and open-source software that each cover some subset of the infrastructure requirements to get ML to production. In this session - we’ll focus on the two most important platforms: model management platforms and feature stores. Model management platforms such as Kubeflow help you get models to production quickly and reliably. Feature stores help you easily build, use, and deploy features. Together, they cover requirements to get models and data to production - the two most important components of any ML project.   In this panel discussion, we’ll be joined by David Aronchick (Co-Founder of Kubeflow), Mike Del Balso (Co-Founder of Tecton) and Willem Pienaar (Creator of Feast). These experts will share their perspective on the challenges of Operational ML and how to build the ideal infrastructure stack for MLOps. They’ll talk about the importance of managing models and data with the same engineering efficiency and best practices that we’ve been applying to application code. They’ll discuss the role of Kubeflow, Feast and Tecton, and share their views on the future of MLOps tooling. // Bio: Michael Del Balso CEO & Co-founder, Tecton   Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto. Willem Pienaar Co-creator, Feast   Willem is currently a tech lead at Tecton where he leads the development of Feast, an open-source feature store for machine learning. Previously he led the ML platform team at Gojek, the Southeast Asian decacorn, which supports a wide variety of models and handles over 100 million orders every month. His main focus areas are building data and ML platforms, allowing organizations to scale machine learning and drive decision making. In a previous life, Willem founded and sold a networking startup. David Aronchick Program Manager, Azure Innovations   David leads works in the Azure Innovation Office on Machine Learning. This means he spends most of my time helping humans to convince machines to be smarter. He is only moderately successful at this.   Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. He has also worked at Microsoft, Amazon, and Chef and co-founded three startups.   ----------- 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 Michael on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/ Connect with Willem on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/ Connect with David on LinkedIn: https://www.linkedin.com/in/aronchick/