Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this one-day course, you'll learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.
This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks
In this module, you will learn how to provision an Azure Databricks workspace and cluster, and use them to work with data.
Getting Started with Azure Databricks
Working with Data in Azure Databricks
After completing this module, you will be able to:
Provision an Azure Databricks workspace and cluster
Use Azure Databricks to work with data
In this module, you will learn how to use Azure Databricks to prepare data for modeling, and train and validate a machine learning model.
Preparing Data for Machine Learning
Training a Machine Learning Model
After completing this module, you will be able to use Azure Databricks to:
Prepare data for modeling
Train and validate a machine learning model
In this module, you will learn how to use MLflow to track experiments running in Azure Databricks, and how to manage machine learning models.
Using MLflow to Track Experiments
Managing Models
After completing this module, you will be able to:
Use MLflow to track experiments
Manage models
In this module, you will learn how to integrate Azure Databricks with Azure Machine Learning
Tracking Experiments with Azure Machine Learning
Deploying Models
After completing this module, you will be able to:
Run Azure Machine Learning experiments on Azure Databricks compute
Deploy models trained on Azure Databricks to Azure Machine Learning
Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts. Before attending this course, complete the learning path on Microsoft Learn:1613 - Create Machine Learning Models
