Implementing a Machine Learning solution with Azure Databricks (DP-3014)

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