Azure Machine Learning Step 2: ETL & Datasets

In this second session of the Azure Machine Learning series, we’ll move beyond the high‑level overview and dive into one of the most essential parts of any ML workflow: preparing and managing your data. Azure Machine Learning provides powerful tools for ingesting, transforming, and organizing datasets so you can build reliable, repeatable machine learning pipelines.
This session focuses on the practical side of ETL (Extract, Transform, Load) within Azure ML and how to work effectively with datasets in the platform. Whether you’re coming from Step 1 or already familiar with the basics, this meetup will help you understand how data flows through Azure ML and how to set up your environment for successful model training.
You’ll learn:

• How ETL fits into the machine learning lifecycle
• Options for ingesting data into Azure ML (local files, cloud storage, data stores)
• How to create, version, and manage Azure ML Datasets
• Techniques for transforming and preparing data using the Studio UI, SDK, and pipelines
• Best practices for organizing data for reproducibility and collaboration
• How prepared datasets connect to training jobs and experiments

This session is designed to give you the confidence to start building real ML workflows with clean, well‑structured data. If you’re ready to move from “What is Azure ML?” to “How do I actually use it with my data?”, this is your next step.