Deployment Isn't the Final Step: Monitoring Machine Learning Models in Production Environments

What you'll learn

Whether it is auto-translation, auto-completion, face or voice recognition, recommendation systems or autonomous driving, AI-based systems can be found in almost every aspect of our daily lives. Although the development of learning systems has become common among companies and a number of methodologies have been developed around them, there is still a lot of confusion around the deployment of those systems in a production environment - whose responsibility it is and most importantly who monitors those models once they are deployed. In this session, we will talk about the data science project cycle which holds five main stages - defining your project objectives, collecting and cleaning your data, training and testing a predictive model, deploying it in a production environment and monitoring its actions and decisions. We will then concentrate on the last forgotten stage, which is critical for DevSecOps teams, and see why monitoring those systems is crucial for organizations using real-life examples from recent years of AI-based systems that went crazy when they were deployed without any supervision.

Table of contents