1 – Explore Azure Databricks

• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts
• Data governance using Unity Catalog and Microsoft Purview
• Module assessment

2 – Use Apache Spark in Azure Databricks

• Get to know Spark
• Create a Spark cluster
• Use Spark in notebooks
• Use Spark to work with data files
• Visualize data
• Module assessment

3 – Train a machine learning model in Azure Databricks

• Understand principles of machine learning
• Machine learning in Azure Databricks
• Prepare data for machine learning
• Train a machine learning model
• Evaluate a machine learning model
• Module assessment

4 – Use MLflow in Azure Databricks

• Capabilities of MLflow
• Run experiments with MLflow
• Register and serve models with MLflow
• Module assessment

5 – Tune hyperparameters in Azure Databricks

• Optimize hyperparameters with Hyperopt
• Review Hyperopt trials
• Scale Hyperopt trials
• Module assessment

6 – Use AutoML in Azure Databricks

• What is AutoML?
• Use AutoML in the Azure Databricks user interface
• Use code to run an AutoML experiment
• Module assessment

7 – Train deep learning models in Azure Databricks

• Understand deep learning concepts
• Train models with PyTorch
• Distribute PyTorch training with TorchDistributor
• Module assessment

8 – Manage machine learning in production with Azure Databricks

• Automate your data transformations
• Explore model development
• Explore model deployment strategies
• Explore model versioning and lifecycle management
• Module assessment