Model governance and model operations: building and deploying robust, production-ready machine learning models

O'Reilly's surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. A few factors are contributing to this strong interest in implementing ML in products and services. First, the machine learning community has conducted groundbreaking research in many areas of interest to companies, and much of this research has been conducted out in the open via preprints and conference presentations. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Organizations now also have more use cases and case studies from which to draw inspiration—no matter what industry or domain you are interested in, chances are there are many interesting ML applications you can learn from. Finally, modeling tools are improving, and automation is beginning to allow new users to tackle problems that used to be the province of experts.



With the shift toward the implementation of machine learning, it’s natural to expect improvement in tools targeted at helping companies with ML. In previous posts, we’ve outlined the foundational technologies needed to sustain machine learning within an organization, and there are early signs that tools for model development and model governance are beginning to gain users.

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