Module 1 : Explore Azure Databricks

• • Provision an Azure Databricks workspace.
• • Identify core workloads and personas for Azure Databricks.
• • Use Data Governance tools Unity Catalog and Microsoft Purview
• • Describe key concepts of an Azure Databricks solution.

Module 2 : Use Apache Spark in Azure Databricks

• • Describe key elements of the Apache Spark architecture.
• • Create and configure a Spark cluster.
• • Describe use cases for Spark.
• • Use Spark to process and analyze data stored in files.
• • Use Spark to visualize data.

Module 3 : Train a machine learning model in Azure Databricks

• • Prepare data for machine learning
• • Train a machine learning model
• • Evaluate a machine learning model

Module 4 : Use MLflow in Azure Databricks

• • Use MLflow to log parameters, metrics, and other details from experiment runs.
• • Use MLflow to manage and deploy trained models.

Module 5 : Tune hyperparameters in Azure Databricks

• • Use the Hyperopt library to optimize hyperparameters.
• • Distribute hyperparameter tuning across multiple worker nodes.

Module 6 : Use AutoML in Azure Databricks

• • Use the AutoML user interface in Azure Databricks
• • Use the AutoML API in Azure Databricks

Module 7 : Train deep learning models in Azure Databricks

• • Train a deep learning model in Azure Databricks
• • Distribute deep learning training by using the Horovod library

Module 8 : Manage machine learning in production with Azure Databricks

• • Automate feature engineering and data pipelines
• • Model development and training
• • Model deployment strategies
• • Model versioning and lifecycle management