[Report] A comprehensive guide for machine learning governance in the enterprise
Time and again, we hear about the crucial importance of machine learning governance for enterprises. Governance is a necessary investment not just as you scale your ML initiatives, but as soon as you invest in your very first model. Effective governance not only protects your business but also maximizes your ROI from ML.
And yet, hardly anyone is implementing governance with ease. According to our 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.
The challenge is that while a lot of people are talking about governance, very little prescriptive advice exists for how to implement it in practice. That’s why, today, we’re excited to share the publication of a new report from O’Reilly, The Framework for ML Governance. Download it for free now.
The report presents a comprehensive framework for governing ML models in the enterprise, all the way from development to production. Download it today to learn:
- Why organizations aren’t seeing value from ML (hint: governance holds the key to your ML’s business value)
- The relationship between MLOps and ML governance, and how it applies across the full ML lifecycle
- Every component needed to effectively govern ML during each stage of the ML lifecycle
- How to set up an ML governance program, including who to involve and how to involve them
- Expert tips for implementing the framework successfully at your unique business
So what are you waiting for? If you want to maximize the business impact of your ML investments, you need effective governance. Download the report to set yourself up for success today.
Looking for even more ML governance resources? Check out our blog series for more tips, tools, and walkthroughs.
More from the ML governance blog series
- What is model governance?
- Why you need machine learning governance: Governor Brainard’s recent speech about responsible AI
- Why governance should be a crucial component of your 2021 ML strategy
- What you need to know about model risk management
- Why risk managers need to improve governance of AI in 2021
- Model drift and ensuring a healthy machine learning lifecycle
- The value of model accuracy
- Introduction to optimizers
- Introduction to loss functions
- How to version control your production machine learning models