Learn more about the ML Ops Community: https://mlops.community/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:45 Intro04:10 How I got into data science and machine learning08:25 My experience working as an ML engineer and starting the podcast12:15 Project management methods for machine learning20:50 ML job roles are trending towards more specialization26:15 ML tools enable collaboration between roles and encode best practices34:00 Data privacy, security, and provenance as first class considerations39:30 The future of managed ML platforms and cloud providers49:05 What I've learned about building a career in ML engineering54:10 Dealing with information overloadLinks:Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in ProductionThe Third Wave Data ScientistPractical ML Ops // Noah Gift // MLOps Coffee SessionsBuilding a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)SRE for ML Infra // Todd Underwood // MLOps Coffee SessionsLuigi Patruno on the ML Ops Community podcastLuigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"