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Course Catalog
Implementing a Machine Learning Solution with Microsoft Azure Databricks (DP-090T00)
Code: MDP-090T00
Duration: 1 Day
$795 USD

OVERVIEW

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.

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DELIVERY FORMAT

This course is available in the following formats:

Virtual Classroom

Duration: 1 Day
Classroom

Duration: 1 Day

CLASS SCHEDULE
Call 800-798-3901 to enroll in this class!

GOALS

Students will learn to,

  • Explore Azure Databricks
  • Use Apache Spark in Azure Databricks
  • Train a machine learning model in Azure Databricks
  • Use MLflow in Azure Databricks
  • Track Azure Databricks experiments in Azure Machine Learning
  • Deploy Azure Databricks models in Azure Machine Learning
OUTLINE

Module 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

Module 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
LABS

  • Lab : Getting Started with Azure Databricks
  • Lab : Working with Data in Azure Databricks
  • Lab : Training a Machine Learning Model
  • Lab : Preparing Data for Machine Learning
  • Lab : Using MLflow to Track Experiments
  • Lab : Managing Models
  • Lab : Deploying Models in Azure Machine Learning
  • Lab : Running Experiments in Azure Machine Learning
  • Lab : Getting Started with Azure Databricks
  • Lab : Working with Data in Azure Databricks
  • Lab : Training a Machine Learning Model
  • Lab : Preparing Data for Machine Learning
  • Lab : Using MLflow to Track Experiments
  • Lab : Managing Models
  • Lab : Deploying Models in Azure Machine Learning
  • Lab : Running Experiments in Azure Machine Learning
WHO SHOULD ATTEND

This 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.

PREREQUISITES

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 following learning path on Microsoft Learn: