📕 If only someone wrote the book on ML Observability*
Was this email forwarded to you? Sign up here Getting a model from research to production is hard. So is keeping it there as performance degradation can happen anytime. How can you quickly get to the bottom of issues in a post-COVID world that makes easy insights difficult? Arize AI wrote a book on how to answer that question. ML Observability 101 covers everything from how to get to the bottom of model performance issues to primers on explainability, drift and service health. Want to hear how one company is tackling ML monitoring and observability or ask questions live? Join us for a foray into ML observability in lending, featuring a fireside chat with Richard Woolston, Data Science Manager at America First Credit Union. *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#146: A Deep Dive Into Arize AI ML Observability Platform
Thursday, December 2, 2021
While the number of platforms that incorporate observability as a native capability is still small, the relevance of this feature in ML applications has been progressively increasing
📝 Guest post: A Guide to Leveraging Active Learning for Data Labeling
Wednesday, December 1, 2021
by Labelbox
🔬 Edge#145: MLOPs – model observability
Tuesday, November 30, 2021
plus an architecture for debugging ML models and overview of Arize AI
⚡️LAST DAY: 30% OFF⚡️
Monday, November 29, 2021
for the Premium subscription
👨🏼🎓👩🏽🎓 The Standard for Scalable Deep Learning Models
Sunday, November 28, 2021
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