Machine Learning Ops Roundup
ml-opsWhy is machine learning in the real world hard and how do you make it better? This newsletter brings together the best articles, news, and papers highlighting the challenges and opportunities in MLOps
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 32 | Subscribers | Read | mlopsroundup.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
Issue #31: Refuel.ai. LLMs can label data better than humans. Autolabel.
Welcome to the 31st issue of the MLOps newsletter (hint: it’s a special one!).
Issue #30.5: Special Announcement - A new course on MLOps!
Welcome to the latest issue of the MLOps newsletter. In this special (mini) edition of the newsletter we have some exciting news to share!
Issue #30: ML Platforms. On Deck Data Science. Explainability in Healthcare. Re:Invent. ML for Content Moderation.
Happy New Year and welcome to the 30th issue of the MLOps newsletter. We have been on a bit of a hiatus from the newsletter (sorry that we can’t share the reasons just yet 😄), but will be back on a regular cadence now!
Issue #29: State of AI. Kaggle ML Survey. ML Deployment at Reddit. Inferentia.
Welcome to the 29th issue of the MLOps newsletter. This one is going to be fairly image-rich - hope you enjoy it! 🖼️
Authors
The writers behind this newsletter.
Why is ML in the real world difficult? This newsletter brings together the best articles, news and papers. By @rish_bhargava and @nihit_desai.
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