Data Science and Machine Learning in Fabric

Modern data teams need a unified environment to transition seamlessly from raw data to production-ready machine learning models. Fabric provides a single, cohesive platform that integrates data engineering, data science, and AI workflows without the friction of managing multiple disconnected services. This course guides you from the fundamental concepts of data science to deploying machine learning models directly within Fabric. You will learn how to ingest data, perform exploratory analysis, train models, and manage the machine learning lifecycle through clear, step-by-step written explanations and practical code examples. What you'll learn: - Understand the foundational components of the Fabric data science workspace - Write and execute Python code in Fabric notebooks to clean and prepare data - Train machine learning models using popular open-source libraries - Track experiments and log model metrics with MLflow integration - Register and deploy trained models for batch and real-time scoring - Apply modern data workflows, including semantic link integration and efficient dataframe operations The course begins with essential definitions and an introduction to the Fabric ecosystem, ensuring you understand the interface and architecture before diving into code. You will then progress through the entire data science lifecycle, from initial data exploration to model deployment and monitoring. This text-based course is designed for aspiring data scientists, data analysts, and developers who want to leverage Fabric for machine learning. No prior experience with Fabric is required, though a basic familiarity with Python is helpful. Start your journey into unified data science and begin building machine learning models in Fabric today.