In the development of machine learning, we have reached a milestone in which technology transcends the boundaries of theory and academics and moves into the “real world”, that is, to enterprises that offer all kinds of services and products to people around the world. While this change is exciting, it is also challenging, as it combines the complexity of machine learning models with the complexity of modern organization.

One of the challenges organizations face as they move from experimenting with machine learning to scaling in production environments is maintenance. How can companies go from managing a single model to managing tens, hundreds, or even thousands of models? This is not only where MLOPS comes in, but also where the aforementioned complexities arise, both on the technical and business side. This guide introduces readers to the challenges in question, while also providing practical information and solutions for improving MLOps capabilities.

So what will you learn when you read this document?

• What is MLOPs and why is it so important for successful ML deployment?
• How are roles and responsibilities in an MLOps team defined and managed?
• What are the best practices for model development, deployment, monitoring, and maintenance?
• How to implement effective governance and compliance strategies for MLOPs
• What are real-world examples of MLOPS being implemented in various industries?