📕 If only someone wrote the book on ML Observability*Getting a model from research to production is hard.
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🔺 Edge#146: A Deep Dive Into Arize AI ML Observability Platform While the number of platforms that incorporate observability as a native capability is still small, the relevance of this feature in ML applications ha…
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📝 Guest post: A Guide to Leveraging Active Learning for Data Labelingby Labelbox
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🔬 Edge#145: MLOPs – model observabilityplus an architecture for debugging ML models and overview of Arize AI
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👨🏼🎓👩🏽🎓 The Standard for Scalable Deep Learning ModelsWeekly news digest curated by the industry insiders
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▪️▫️▪️▫️ Edge#144: How Many AI Neurons Does It Take to Simulate a Brain Neuron?A new research shows some shocking answers to that question
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🎙 TheSequence Chat: a year of interesting conversationsShare this collection if you find it enriching. No subscription is needed.
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🏗 Edge#143: Feature Stores in ML Pipelines: A RecapThis holiday week, let’s have some time to catch up with what we’ve covered before. As part of our current MLOps series, we offer you the recap of arti…
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♟♟ Chess Learning Explainability Weekly news digest curated by the industry insiders
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🤖Edge#142: How Microsoft Built a 530 Billion Parameter ModelWhat’s New in AI, a deep dive into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you u…
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☝️ CoreWeave is allocating $50 million to scale the best compute infrastructure for ML*How are they going to do it? Read along
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🩺 Edge#141: MLOPs – Model Monitoringplus the building blocks of interpretability and a few ML monitoring platforms to keep up with
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