Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
Course Outline
Lesson 1: 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
- Knowledge check
- Summary
Lesson 2: 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
- Knowledge check
- Summary
Lesson 3: 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
- Knowledge check
- Summary
Lesson 4: 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
- Knowledge check
- Summary
Lesson 5: Tune hyperparameters in Azure Databricks
- Introduction
- Optimize hyperparameters with Hyperopt
- Review Hyperopt trials
- Scale Hyperopt trials
- Exercise – Optimize hyperparameters for machine learning in Azure Databricks
- Knowledge check
- Summary
Lesson 6: 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
- Knowledge check
- Summary
Lesson 7: 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
- Knowledge check
- Summary
Lesson 8: 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
- Knowledge check
- Summary
