Learn how to test & monitor production machine learning models.
What is model testing?
You’ve taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren’t any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML–specific unit, integration and differential tests can help you to minimize the risk.
What is model monitoring?
You’ve deployed your model to production. OK now what? Is it working as you expect? How do you know? By monitoring models, we can check for unexpected changes in:
When we think about data science, we think about how to build machine learning models, which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. However, how we are going to actually test & monitor these models in a production system is often neglected, . Only when we can effectively monitor our production models can we determine if they are performing as we expect.
Why take this course?
This is the first and only online course where you can learn how to test & monitor machine learning models. The course is comprehensive, and yet easy to follow. Throughout this course you will learn all the steps and techniques required to effectively test & monitor machine learning models professionally.
Courses : 1
Specification: Testing and Monitoring Machine Learning Model Deployments
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