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
