Built as a joint effort by Microsoft and the team that started Apache Spark, Azure Databricks provides data science, engineering, and analytical teams with a single platform for big data processing and machine learning. In this course, you'll learn how to use Azure Databricks to train and deploy machine learning models. Module 1 : Explore Azure DatabricksProvision an Azure Databricks workspace.Identify core workloads and personas for Azure Databricks.Use Data Governance tools Unity Catalog and Microsoft PurviewDescribe key concepts of an Azure Databricks solution.Module 2 : Use Apache Spark in Azure DatabricksDescribe 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 DatabricksPrepare data for machine learningTrain a machine learning modelEvaluate a machine learning modelModule 4 : Use MLflow in Azure DatabricksUse MLflow to log parameters, metrics, and other details from experiment runs.Use MLflow to manage and deploy trained models.Module 5 :
