Course Overview
TOPIn 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.
Scheduled Classes
TOPOutline
TOPModule 1: Explore Azure Databricks
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- 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: Track Azure Databricks experiments in Azure Machine Learning
- Describe Azure Machine Learning
- Run Azure Databricks experiments in Azure Machine Learning
- Log metrics in Azure Machine Learning with MLflow
- Run Azure Machine Learning pipelines on Azure Databricks compute
Module 6: Deploy Azure Databricks models in Azure Machine Learning
- Describe considerations for model deployment
- Plan for Azure Machine Learning deployment endpoints
- Deploy a model to Azure Machine Learning
- Troubleshoot model deployment
Prerequisites
TOPBefore 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 following learning path on Microsoft Learn:
Who Should Attend
TOPThis course is designed for data scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks.