Episode 19
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/
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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
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"
