Implementing a Machine Learning solution with Azure Databricks (DP-3014) Virtual Classroom Live May 26, 2026

Course Overview

1 - Explore Azure Databricks

• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts

2 - Use Apache Spark in Azure Databricks

• Get to know Spark
• Create a Spark cluster
• Use Spark in notebooks
• Use Spark to work with data files
• Visualize data

3 - Train a machine learning model in Azure Databricks

• 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

4 - Use MLflow in Azure Databricks

• Capabilities of MLflow
• Run experiments with MLflow
• Register and serve models with MLflow

5 - Tune hyperparameters in Azure Databricks

• Optimize hyperparameters with Hyperopt
• Review Hyperopt trials
• Scale Hyperopt trials

6 - Use AutoML in Azure Databricks

• What is AutoML?
• Use AutoML in the Azure Databricks user interface
• Use code to run an AutoML experiment

7 - Train deep learning models in Azure Databricks

• Understand deep learning concepts
• Train models with PyTorch
• Distribute PyTorch training with Horovod
• Module assessment

8 - Manage machine learning in production with Azure Databricks

• Automate your data transformations
• Explore model development
• Explore model deployment strategies
• Explore model versioning and lifecycle management
• Module assessment

Prerequisites

This learning path assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.