Advanced Machine Learning with Databricks

Overview

🕮 Off the shelf (OTS)

This course is designed for data scientists and machine learning practitioners seeking to scale machine learning workflows and implement MLOps best practices using Databricks. The course is delivered over two four-hour modules, covering Apache Spark for ML, hyperparameter tuning with Optuna, and MLOps automation with Databricks tools such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving.
Participants will gain hands-on experience with Spark ML, pandas APIs on Spark, MLflow, and Unity Catalog, ensuring effective model tracking, governance, and deployment.

Participants should have:
• Basic knowledge of data science and machine learning concepts (e.g., classification and regression models).
• Familiarity with common ML evaluation metrics (e.g., F1-score).
• Experience with Python and ML libraries (e.g., scikit-learn, XGBoost).
• Intermediate-level knowledge of ML development and the use of Git for ML projects.

If you do not have one or more of the pre-requisites we recommend: Apache Spark Programming with Databricks.

This course is designed for:
• Data scientists and ML engineers who want to scale machine learning workflows with Databricks.
• MLOps practitioners aiming to streamline ML lifecycle management, testing, and deployment.
• AI/ML professionals implementing CI/CD, model monitoring, and production-ready ML systems.

This course includes:
• Hands-on labs with Spark ML, Optuna tuning, and MLflow tracking.
• Practical exercises on CI/CD pipelines, model packaging, and governance.
• Instructor-led demonstrations of model deployment and monitoring techniques.

This course is not specifically aligned with an exam.