Implementing Data Science and Machine Learning with Fabric

Modern organizations need unified platforms to turn raw data into actionable predictions without the complexity of managing disjointed systems. Fabric provides an all-in-one environment designed to streamline this entire lifecycle from data ingestion to model deployment. In this text-based course, you will transition from understanding core data science environments to configuring fully functional machine learning solutions. You will learn how to orchestrate data, explore datasets, and track experiments efficiently using modern cloud data tools. The curriculum emphasizes practical understanding and conceptual mastery of collaborative data workflows. What you'll learn: - Understand the core architecture of Fabric, including workspaces, lakehouses, and Delta tables. - Explore and preprocess data using Synapse notebooks and Spark. - Train machine learning models and track experiments using MLflow. - Apply modern AI integration patterns, including basic generative AI workflows. - Deploy and register models to generate batch predictions for business intelligence. The course begins with foundational concepts, key terminology, and environment setup before moving into data exploration, model training, and deployment strategies. You will learn through clear, written explanations and structured code snippets designed for self-paced reading. This course is designed for aspiring data scientists, data analysts, and developers looking to transition into modern AI workflows. No prior experience with Fabric is required, though a basic understanding of Python is helpful. Start reading today to unlock the potential of collaborative data science.