Chuyển sang chế độ ngoại tuyến với ứng dụng Player FM !
Lessons Learned From Hosting the ML Engineered Podcast (Charlie Interviewed on the ML Ops Community podcast)
Manage episode 283940016 series 2849409
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
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"
32 tập
Manage episode 283940016 series 2849409
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
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"
32 tập
Tất cả các tập
×Chào mừng bạn đến với Player FM!
Player FM đang quét trang web để tìm các podcast chất lượng cao cho bạn thưởng thức ngay bây giờ. Đây là ứng dụng podcast tốt nhất và hoạt động trên Android, iPhone và web. Đăng ký để đồng bộ các theo dõi trên tất cả thiết bị.