Databricks Machine Learning Fundamentals

In this course, you will learn the fundamentals of using Databricks for machine learning. You will tackle the challenge of disjointed tools and master production-grade machine learning on Databricks. This course guides you through the complete end-to-end ML lifecycle on a single platform, giving you the practical skills to build robust, deployable solutions. You'll start by building a solid data foundation, using Apache Spark to ingest, clean, and engineer high-quality features. Next, master MLOps by using MLflow to systematically track and compare experiments, bringing reproducibility and rigor to your workflow to identify the best model. Finally, close the loop by deploying your models into production. You will use the MLflow Model Registry for versioning and governance before deploying your model as a live, real-time REST API endpoint. Through a series of hands-on labs and a final capstone project, you'll gain the confidence to build, track, and deploy sophisticated ML models, leaving with a portfolio-ready project that makes you a more effective and valuable data professional. This course is designed for intermediate learners who are familiar with basic machine learning concepts and want to learn how to apply them in Databricks for real-world projects. Learners should have a basic understanding of Python, including Pandas and Scikit-learn, along with fundamental machine learning concepts. By the end of this course, learners will be able to apply the full ML lifecycle on the Databricks platform, from data preparation and analysis to model deployment. They will also gain the skills to track experiments and manage models using Databricks and MLflow, ensuring a streamlined, reproducible workflow. Additionally, learners will be equipped to deploy machine learning models effectively using the MLflow Model Registry and Databricks Model Serving

• Apply the end-to-end ML life cycle for data preparation and analysis within the Databricks platform
• Utilize Databricks and MLflow to systematically track experiments and manage the machine learning model life cycle
• Deploy Machine Learning models effectively using the MLflow Model Registry and Databricks Model Serving

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work