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Drive Efficiency and Productivity with Machine Learning Operations

If you’re a data scientist or machine learning (ML) engineer, Azure Machine Learning is built for you. It has built-in MLOps, making it easy to implement DevOps for machine learning through comprehensive support for all phases of the ML life cycle.

With all that Azure Machine Learning provides, you’ll no longer be forced to work in inefficient ways or be burdened with mundane tasks. Instead, you’ll have the means to streamline and automate the end-to-end ML life cycle and tie it into existing DevOps processes, so you can collaborate with app developers and work at the same cadence when building ML-infused apps.

We wrote this paper for ML professionals who are looking for a better way to work—with access to the same types of sophisticated tooling and streamlined engineering practices that developers working in a modern DevOps environment take for granted.

  • Author(s):
  • Microsoft Azure
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Drive Efficiency and Productivity with Machine Learning Operations
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  • White Paper
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Website:Visit Publisher Website
Publisher:Microsoft
Published:December 5, 2019
License:Copyrighted
Copyright:© Used with permission from Microsoft

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