MLOps
6 Threads
Every #DataEngineer should know what Spark Shuffle is and why it happens. Get more info here: https://t.co/oWhazlzkLX Or read on in the thread 👇 Also subscribe to my newsletter...
This has been such an excellent year for software system design in ML. So, I compiled a list of some of my favorite papers 📜in MLOps. Here are some of my favorite ones till date⤵...
Our understanding of MLOps is limited to a fragmented landscape of thought pieces, startup landing pages, & press releases. So we did interview study of ML engineers to understand...
Machine learning is a vast topic, and the key to unlocking its real potential is efficient MLOps! Here are 5 categories of MLOps tools you would need in production. (A thread) 👇
Data Analysis, Data science, Machine learning, and AI podcasts you should consider listening to👌 - DataTalkClub - TheDataScientistshow. - SuperDatascience - BlackinData - Datap...
I probably should have written this years ago, but here are some MLOps principles I think every ML platform (codebase, data management platform) should have: 1/n