Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production

Coffee Sessions #18  with Luigi Patruno of ML in Production, a Centralized Repository of Best Practices Summary Luigi Patruno and ML in production MLOps workflow: Knowledge sharing and best practices Objective: learn! Links: ML in production: https://mlinproduction.com/ Why you start MLinProduction: https://mlinproduction.com/why-i-started-mlinproduction/ Luigi Patruno: a man whose goal is to help data scientists, ML engineers, and AI product managers, build and operate machine learning systems in production. Luigi shares with us why he started ML in Production - A lot irrelevant content; a lot of clickbait with low standards of quality. He had an Entrepreneurial itch and The solution was to start a weekly newsletter. From there he started creating Blog posts and now teamed up with Sam Charrington of TWIML to create courses on SagMaker ML.  Applied ML Best practices Reading google and microsoft papers Analyzing the tools that are out there ie sagemaker and how to the see the world? Aimed at making you more effective and efficient at your job Community questions Taking some time to answer some community questions! Who do you learn from? Favorite resources? Self-taught, papers, talks Construct the systems Uber michelangelo ----------------- 📝 Rought notes 📝 ---------------- Any companies that stand out to you in terms of MLOps excellence? Google, Amazon, Stichfix: they've had to solve hard problems Serving ads Personalization at scale Vertical problems: within their vertices Motivated by real challenges DropBox Great articles A great machine learning company Tools Sagemaker Has a course on sagemaker Nice lessons baked into the system Dos and don’t of MLOps DO LOG! Monitor Automate - manual analysis leads to problems Do it manually first til you feel confident that you can automate it Tag, version Store your training, val, and test sets! What is his process of identifying use cases that are suitable for machine learning as a solution? How do they proceed methodically? Start with business goal Potential number of users that the solution can benefit The ability to build a predictive model Performance x impact = score Rank problems by this How developed are the datasets? What part of the ML in Production process do people underestimate the most? What are the low hanging fruits that many people don’t take advantage of? Generate actual value without needing to build the most complex model possible In industry, performance is only one part of the equation How has he seen ML in production evolve over the last few years and where does he think it's headed next? More and more tools! Industry-specific tool taking advantage of ML Problem is you must have industry knowledge  --------------- ✌️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/