📝 The Definitive ML Observability Checklist*
Was this email forwarded to you? Sign up here The machine learning infrastructure ecosystem is confusing, crowded and complex. With so many companies making competing claims, it can be easy to get lost. However, the need for better ML observability tools to monitor, troubleshoot, and explain model decisions is clear. This checklist covers the essential elements to consider when evaluating an ML observability platform. Whether you’re readying an RFP or assessing individual platforms, this buyer’s guide can help with product and technical requirements to consider across:
*This post is an offering from our partner Arize AI. We thank the Arize AI team for their ongoing support of TheSequence.
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➗✖️ Edge#148: The OpenAI Model to Solve Text Math Problems
Thursday, December 9, 2021
an interesting method to tackle this type of problem
🎙 Doug Downey/Semantic Scholar: Applying Cutting Edge NLP at scale
Wednesday, December 8, 2021
Fascinating conversation about the vision behind the project, existing capabilities and what can be achieved with commonsense reasoning
🍮 Edge#147: MLOPs – Model Serving
Tuesday, December 7, 2021
plus overview of the TensorFlow serving paper and TorchServe
✴️ Amazon’s Big ML Week
Sunday, December 5, 2021
Weekly news digest curated by the industry insiders
📕 If only someone wrote the book on ML Observability*
Friday, December 3, 2021
Getting a model from research to production is hard.
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