Lessons Learned From Hosting the ML Engineered Podcast (Charlie Interviewed on the ML Ops Community podcast)

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/newsletter

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Subscribe to ML Engineered: https://mlengineered.com/listen

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:45 Intro

04:10 How I got into data science and machine learning

08:25 My experience working as an ML engineer and starting the podcast

12:15 Project management methods for machine learning

20:50 ML job roles are trending towards more specialization

26:15 ML tools enable collaboration between roles and encode best practices

34:00 Data privacy, security, and provenance as first class considerations

39:30 The future of managed ML platforms and cloud providers

49:05 What I've learned about building a career in ML engineering

54:10 Dealing with information overload

Links:

Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production

The Third Wave Data Scientist

Practical ML Ops // Noah Gift // MLOps Coffee Sessions

Building a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)

SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions

Luigi Patruno on the ML Ops Community podcast

Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"