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 PracticesJoin the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠SummaryLuigi Patruno and ML in productionMLOps workflow: Knowledge sharing and best practicesObjective: 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 of relevant 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 MLBest practicesReading Google and Microsoft papersAnalyzing the tools that are out there, ie, Sagemaker, and how to see the world?Aimed at making you more effective and efficient at your jobCommunity questionsTaking some time to answer some community questions!Who do you learn from? Favorite resources?Self-taught, papers, talksConstruct the systemsUber michelangelo----------------- 📝 Rought notes 📝 ----------------Any companies that stand out to you in terms of MLOps excellence?Google, Amazon, Stitchfix: they've had to solve hard problemsServing adsPersonalization at scaleVertical problems: within their verticesMotivated by real challengesDropBoxGreat articlesA great machine learning companyToolsSagemakerHas a course on SagemakerNice lessons baked into the systemDos and don’ts of MLOpsDO LOG!MonitorAutomate - manual analysis leads to problemsDo it manually first til you feel confident that you can automate itTag, versionStore 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 the business goalThe potential number of users that the solution can benefitThe ability to build a predictive modelPerformance x impact = scoreRank problems by thisHow 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 possibleIn the industry, performance is only one part of the equationHow 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 MLThe problem is that you must have industry knowledge --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/