TheSequence - 🍮 Edge#147: MLOPs – Model Serving
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💡 ML Concept of the Day: Model ServingContinuing with our MLOPs series, we would like to focus on the serving of machine learning (ML) models. Model deployment/serving can be considered one of MLOps pipelines' most challenging aspects. This is partly because model serving architectures have little to do with data science and are more related to ML engineering techniques. Some ML models take hours to execute, requiring large computation pipelines, while others can be executed in seconds on a mobile phone. A solid ML serving infrastructure should be able to adapt to diverse requirements from ML applications. Many unique requirements can influence a model serving architecture. Throughout the last few years, we have seen four fundamental model serving patterns emerging in modern ML architectures: ... Subscribe to TheSequence to read the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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✴️ 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.
🔺 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
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