Authors

Subscribe:.rss.atom.json.md

Publishes 1 feed

• Machine Learning Ops Roundup

  Why 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

  10 posts · contributor

Lately

• Issue #31: Refuel.ai. LLMs can label data better than humans. Autolabel.

  Machine Learning Ops Roundup · 2023-06-26

• Issue #30.5: Special Announcement - A new course on MLOps!

  Machine Learning Ops Roundup · 2022-03-18

• Issue #30: ML Platforms. On Deck Data Science. Explainability in Healthcare. Re:Invent. ML for Content Moderation.

  Machine Learning Ops Roundup · 2022-01-24

• Issue #29: State of AI. Kaggle ML Survey. ML Deployment at Reddit. Inferentia.

  Machine Learning Ops Roundup · 2021-10-27

• Issue #28: MAD Landscape. Covid-19 Border Testing. Blocking Spam@Slack. Applying ML. Scikit-learn 1.0.

  Machine Learning Ops Roundup · 2021-10-04

• Issue #27: Medical Imaging Challenges. Machine Unlearning. Managing Supply and Demand. AI Sandbox.

  Machine Learning Ops Roundup · 2021-09-20

• Issue #26: Concept Drift. Anomaly Detection with Self-Supervision. NLP in Legal Applications. Models Per Customer?

  Machine Learning Ops Roundup · 2021-09-07

• Issue #25: Tesla AI Day. Feature Stores. NIST on AI bias. Model monitoring tips. AI and COVID.

  Machine Learning Ops Roundup · 2021-08-23

• Issue #24: AI at Porsche. Efficient Inference. Bootstrapping Labels. Nearest-Neighbor Benchmarks.

  Machine Learning Ops Roundup · 2021-08-09

• Issue #23: AI Regulations. Efficient Active Learning. Model Health Assurance. Vertex Matching Engine.

  Machine Learning Ops Roundup · 2021-07-26

Everything on this page was read from markup Rishabh Bhargava published — a rel="me" link, an h-card, or the feed’s own author element. Nothing was inferred from anywhere else. To correct or remove it, get in touch. Machine-readable: JSON