"Despite the growing adoption of machine learning, research has shown that as many as 50-90% of machine learning models fail to make it into production due to a lack of planning, inadequate data, and the complexity of the models themselves."

This session is designed to provide attendees with an in-depth understanding of how to take a model from POC to production following the entire machine learning lifecycle. From model training to deployment and monitoring, covering all essential topics including feature store, MLflow, testing for accuracy and fairness, code vs model deployment, multiple patterns for batch and real-time deployment, and monitoring for drift.

During this day-long session, attendees will learn about the latest tools available in Databricks and Azure and hints and tips for best practices at every step in the process. They will also have the opportunity to engage in hands-on exercises and real-world examples to reinforce their understanding of the concepts discussed.

After this session, attendees will be equipped with the knowledge and practical skills needed to run successful MLOps projects and overcome the productionisation challenges faced by the industry.