Abstract: 

You’ve created a wicked AI or machine learning model that change the way you do business.

Good job.

But how do you validate your model and monitor it in the long run?

Advanced machine learning and AI models gets more and more powerful. They also tend to become more complicated to validate and monitor. This has a major impact in business’ adoption of models. Initial validation and monitoring are not only critical to ensure the model’s sound performance, but they are also mandatory in some industries like banking and insurance. In this workshop, you will learn the best techniques that can be applied manually or automatically to validate and monitor statistical models.

Techniques below will be discussed and demonstrated to perform a full model validation:

Techniques used for initial validation
· Bias and variance
· Model selection
· Hyperparameters selection
· Model interpretation
· Model robustness to adversarial examples

Techniques used for model monitoring

· Bias and variance consistence
· Model degradation
· Model interpretation consistence

Bio: 

Olivier is a data science expert whose leading field of expertise and cutting-edge knowledge of AI and machine learning led him to support many companies’ digital transformations, as well as implementing projects in different industries. He has led the data team and put in place a data culture in companies like Pratt & Whitney Canada, L’Oréal, GSoft and now Moov AI.