Explore Azure Databricks
In this module, you learn how to:
- Provision an Azure Databricks workspace
- Identify core workloads for Azure Databricks
- Use Data Governance tools Unity Catalog and Microsoft Purview
- Describe key concepts of an Azure Databricks solution
Use Apache Spark in Azure Databricks
In this module, you'll learn how to:
- 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.
Learn how to train machine learning models using Spark and the MLlib library in Azure Databricks.
In this module you'll learn how to:
- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model
Learn how to use MLflow in Azure Databricks to track machine learning experiments and deploy models.
In this module, you'll learn how to:
- Use MLflow to log parameters, metrics, and other details from experiment runs.
- Use MLflow to manage and deploy trained models.
Learn how to use the Optuna library in Azure Databricks to tune machine learning hyperparameters.
In this module, you learn how to:
- Use the Optuna library to optimize hyperparameters.
- Distribute hyperparameter tuning across multiple worker nodes.
Learn how to use AutoML to train optimal machine learning models for your data in Azure Databricks.
In this module, you learn how to:
- Use the AutoML user interface in Azure Databricks
- Use the AutoML API in Azure Databricks
Learn how to use deep learning libraries like PyTorch in Azure Databricks, and to distribute training by using TorchDistributor
In this module, you learn how to:
- Train a deep learning model in Azure Databricks.
- Distribute deep learning training by using TorchDistributor.
Learn how to manage machine learning models in production with Azure Databricks.
In this module you explore:
- Automate feature engineering and data pipelines
- Model development and training
- Model deployment strategies
- Model versioning and lifecycle management

Syllabus

Explore Azure Databricks
- Introduction
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Data governance using Unity Catalog and Microsoft Purview
- Exercise - Explore Azure Databricks
- Module assessment
- Summary
Use Apache Spark in Azure Databricks
- Introduction
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Exercise - Use Spark in Azure Databricks
- Module assessment
- Summary
Train a machine learning model in Azure Databricks
- Introduction
- 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
- Exercise - Train a machine learning model in Azure Databricks
- Module assessment
- Summary
Use MLflow in Azure Databricks
- Introduction
- Capabilities of MLflow
- Run experiments with MLflow
- Register and serve models with MLflow
- Exercise - Use MLflow in Azure Databricks
- Module assessment
- Summary
Tune hyperparameters in Azure Databricks
- Introduction
- Optimize hyperparameters with Optuna
- Review trials
- Scale hyperparameter optimization
- Exercise - Optimize hyperparameters for machine learning in Azure Databricks
- Module assessment
- Summary
Use AutoML in Azure Databricks
- Introduction
- What is AutoML?
- Use AutoML in the Azure Databricks user interface
- Use code to run an AutoML experiment
- Exercise - Use AutoML in Azure Databricks
- Module assessment
- Summary
Train deep learning models in Azure Databricks
- Introduction
- Understand deep learning concepts
- Train models with PyTorch
- Distribute PyTorch training with TorchDistributor
- Exercise - Train deep learning models on Azure Databricks
- Module assessment
- Summary
Manage machine learning in production with Azure Databricks
- Introduction
- Automate your data transformations
- Explore model development
- Explore model deployment strategies
- Explore model versioning and lifecycle management
- Exercise - Manage a machine learning model
- Module assessment
- Summary