DP-3014: Build Machine Learning Solutions using Azure Databricks

• Explore Azure Databricks
• Use Apache Spark in Azure Databricks
• Train a machine learning model in Azure Databricks
• Use MLflow in Azure Databricks
• Tune hyperparameters in Azure Databricks
• Use AutoML in Azure Databricks
• Train deep learning models in Azure Databricks

This course is designed for IT professionals, AI engineers and data scientists.

Module 1: Explore Azure Databricks

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
 
Learning objectives:
In this module, you’ll learn how to

• Provision an Azure Databricks workspace.
• Identify core workloads and personas for Azure Databricks.
• Describe key concepts of an Azure Databricks solution.
   
  Prerequisites:
  Before starting this module, you should have a fundamental knowledge of data analytics concepts. Consider completing Azure Data Fundamentals certification  before starting this module.
   
   
  Module 2: Use Apache Spark in Azure DatabricksAzure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
   
  Learning objectives:
  In this module, you’ll learn how to
• 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.
•  
  Prerequisites:
  Before starting this module, you should have a basic knowledge of Azure Databricks. Consider completing the Explore Azure Databricks   module before this one.
   
   
  Module 3: Train a machine learning model in Azure Databricks

  Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.
   
  Learning objectives:
  In this module, you’ll learn how to

• Prepare data for machine learning.
• Train a machine learning model.
• Evaluate a machine learning model.
•  
  Prerequisites:
  Before starting this module, you should be familiar with Azure Databricks. Consider completing the following modules first:
• Explore Azure Databricks
• Use Apache Spark in Azure Databricks
   
   
  Module 4: Use MLflow in Azure DatabricksMLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.
   
  Learning objectives:
  In this module, you’ll learn how to
• Use MLflow to log parameters, metrics, and other details from experiment runs.
• Use MLflow to manage and deploy trained models.
•  
  Prerequisites:
  Before starting this module, you should be familiar with Azure Databricks and the machine learning model training process.
   
   
  Module 5: Tune hyperparameters in Azure Databricks

  Tuning hyperparameters is an essential part of machine learning. In Azure Databricks, you can use the Hyperopt library to optimize hyperparameters automatically.
   
  Learning objectives:
  In this module, you’ll learn how to

• Use the Hyperopt library to optimize hyperparameters.
• Distribute hyperparameter tuning across multiple worker nodes
•  
  Prerequisites:
  Before starting this module, you should be familiar with how to train machine learning models in Azure Databricks.
   
   
  Module 6: Use AutoML in Azure Databricks

  AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.
   
  Learning objectives:
  In this module, you’ll learn how to

• Use the AutoML user interface in Azure Databricks
• Use the AutoML API in Azure Databricks
•  
  Prerequisites:
  Before starting this module, you should be familiar with machine learning in Azure Databricks.
   
   
  Module 7: Train deep learning models in Azure Databricks

  Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.
   
  Learning objectives:
  In this module, you’ll learn how to

• Train a deep learning model in Azure Databricks
• Distribute deep learning training by using the Horovod library
•  
  Prerequisites:
  Before starting this module, you should be familiar with machine learning on Azure Databricks.

Familiarity with machine learning concepts and Azure Databricks basics.
Please review the prerequisites listed for each module in the course content for more information.