Building machine learning models is only the beginning. In this hands-on session, you’ll learn how to build trustworthy and explainable ML workflows using Azure Machine Learning Pipelines and Responsible AI (RAI) Dashboards.
Through a practical end-to-end example, we’ll automate data preprocessing, model training, and responsible model evaluation using Azure’s RAI tooling — all orchestrated through a scalable, modular pipeline.
Whether you're new to MLOps or aiming to embed responsible AI practices into your production workflows, this session will help you turn best practices into reality.

Key Takeaways:

Understand how to design trustworthy, explainable ML workflows using Azure ML Pipelines.

Learn to automate data preprocessing, model training, and responsible model evaluation with RAI tools.

See how scalable, modular pipelines can embed Responsible AI principles across the ML lifecycle.

Gain hands-on experience leveraging Azure ML Studio and Azure AI Foundry ecosystem services.

Discover practical strategies for integrating fairness, explainability, and error analysis into production-grade ML systems.