The Framework of ML Governance - OReilly Report

A comprehensive guide for enterprise machine learning governance

Effective governance is a crucial component of every successful machine learning initiative. Yet, according to Algorithmia's 2021 enterprise trends in machine learning report, governance-related issues present the #1 roadblock for organizations implementing ML. 56% of all organizations struggle with governance, security, and auditability issues, and 67% must comply with multiple regulations for their machine learning.

While there may be a lot of talk about governance, very little prescriptive advice exists for how to implement it in practice. Introducing the first comprehensive governance report: The Framework for ML Governance.

Download the report for free now:

You will learn

  1. Why organizations aren’t seeing value from ML
  2. The relationship between MLOps and ML governance, and how it applies across the full ML lifecycle
  3. Every component needed to effectively govern ML during each stage of the ML lifecycle
  4. How to set up an ML governance program, including who to involve and how to involve them
  5. Expert tips for implementing the framework successfully at your unique business
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Looking for even more ML governance resources? Check out our blog series for more tips, tools, and walkthroughs.

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