Module 1: Explore Azure Databricks

• Introduction to Azure Databricks as a scalable cloud platform for data analytics.
• Use of Apache Spark in Azure Databricks for data transformations, analysis, and visualizations.

Module 2: Train a Machine Learning Model in Azure Databricks

• Understand how Azure Databricks is used for training predictive models.
• Overview of supported machine learning frameworks.

Module 3: Use MLflow in Azure Databricks

• Introduction to MLflow, an open-source platform for managing the machine learning lifecycle.
• Explore MLflow’s native support within Azure Databricks.

Module 4: Tune Hyperparameters in Azure Databricks

• Understand the importance of hyperparameter tuning in machine learning.
• Use the Hyperopt library for automated hyperparameter optimization in Azure Databricks.

Module 5: Use AutoML in Azure Databricks

• Overview of AutoML for simplifying machine learning model building.
• Learn how AutoML integrates within the Azure Databricks ecosystem.

Module 6: Train Deep Learning Models in Azure Databricks

• Introduction to deep learning and its use of neural networks.
• Train deep learning models for advanced AI workloads such as forecasting, computer vision, and natural language processing.