Beyond the Sandbox: Practical MLOps for the Deployed Data Scientist

The true test of a predictive model begins after it leaves the laboratory. While many organizations successfully develop proof-of-concept models, far fewer succeed in deploying, operationalizing, and scaling those models to deliver sustained business value. The challenge is rarely the algorithm itself. More often, it lies in bridging the gap between data science, enterprise data management, and operational execution.
Join co-authors Ankit Anand, Scott Burk, and Kinshuk Dutta for an interactive exploration of the key concepts presented in The Deployed Data Scientist: MLOps and Analytics in Practice.

Through practical examples and an engaging storytelling approach, this session will demystify the complexities of Machine Learning Operations (MLOps) and provide a roadmap for moving from experimentation to reliable production deployment.

Participants will discover how strong data management practices, robust data quality controls, and effective governance frameworks form the foundation of successful AI and analytics initiatives. The discussion will also examine how organisations can monitor, manage, and evolve their models to ensure long-term performance, trust, and business impact.

Whether you are a data scientist, data engineer, architect, data manager, or business leader responsible for AI initiatives, this webinar will provide practical guidance for deploying analytics solutions that are not only innovative, but also scalable, governed, and built to last.