Whitepaper ·PDF
Managing Machine Learning Workflow with Amazon SageMaker: New Services, Use Cases, and Best Practices
A practical playbook for running ML workflows on Amazon SageMaker at production scale.
Artificial intelligence and machine learning have a major impact across markets. Businesses want to drive change with AI, yet their IT and engineering teams face real challenges managing the complex, nuanced tools and services that make ML work at production scale.
To address those practitioner challenges, AWS released Amazon SageMaker Studio — a web-based IDE that tightly integrates the components of the ML ecosystem within a single interface, from experimentation to deployment and monitoring.
What’s inside
- Benefits, features, and use cases of Amazon SageMaker Studio
- A walkthrough of Amazon SageMaker Experiments, Debugger, Autopilot, Model Monitor, and Elastic Inference
- How each of these services works in practice, and where they fit in a production ML workflow
ML engineering, MLOps, and platform leaders standardizing how their teams build, train, deploy, and monitor models on AWS.
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