This presentation will cover what organisations need to understand and have in place in order to deploy ML models at scale while minimising risk. Tom Farrand will discuss the common challenges faced by organisations when scaling ML model deployment and how to overcome them. He will discuss different use cases at different stages of ML maturity, as organisations move into deploying explainers and monitoring alongside their models.

Tom will then walk through the creation, deployment, monitoring and management of a ML model using a real life use case and covering a range of practical tasks including training models on pre-processed datasets, deploying model artefacts with Seldon Deploy and using explainers and outlier detection to gain insight into model decisioning.