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:
- Incoming data
- Model quality
- System operations
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.
In this course, you will have at your fingertips the sequence of steps that you need to follow to test & monitor a machine learning model, plus a project template with full code, that you can adapt to your own models
This is the second course I am completing from Christopher and Soledad. They have the most comprehensive courses on deploying machine learning in an industrial settings that I have taken. As a data scientist, I found the material I learned from these courses directly applicable to my daily work, and many of the tools they introduce either something I use on a daily basis or something I am going to look into using on a daily basis. Perhaps the most valuable course I have done ... (I have completed 30++ courses)
Still Not Sure If The Course Is For You?
Still not sure if this is the right course for you?
Here are some rough guidelines
Never written a line of code before: This course is unsuitable
Never written a line of Python before: This course is unsuitable
Never trained a machine learning model before: This course is unsuitable. Ideally, you have already built a few machine learning models, either at work, or for competitions or as a hobby.
Never used docker before: The second part of the course will be very challenging. You need to be ready to read up on lecture notes & references.
Have only ever operated in the research environment: This course will be challenging, but if you are ready to read up on some of the concepts we will show you, the course will offer you a great deal of value.
Have a little experience writing production code: There may be some unfamiliar tools which we will show you, but generally you should get a lot from the course.
Non-technical: You may get a lot from just the theory lectures, so that you get a feel for the challenges of ML testing & monitoring, as well as the lifecycle of ML models. The rest of the course will be a stretch.
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