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Build machine learning solutions using Azure Databricks (DP-3014)

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.

GK# 834020 Vendor# DP-3014
Vendor Credits:
  • Global Knowledge Delivered Course
  • Training Exclusives
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Who Should Attend?

Data scientists and machine learning engineers.

What You'll Learn

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
  • Tune hyperparameters in Azure Databricks
  • Use AutoML in Azure Databricks
  • Train deep learning models in Azure Databricks
  • Manage machine learning in production with Azure Databricks

Course Outline

Module 1 : Explore Azure Databricks

  • Provision an Azure Databricks workspace.
  • Identify core workloads and personas for Azure Databricks.
  • Use Data Governance tools Unity Catalog and Microsoft Purview
  • 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 : Tune hyperparameters in Azure Databricks

  • Use the Hyperopt library to optimize hyperparameters.
  • Distribute hyperparameter tuning across multiple worker nodes.

Module 6 : Use AutoML in Azure Databricks

  • Use the AutoML user interface in Azure Databricks
  • Use the AutoML API in Azure Databricks

Module 7 : Train deep learning models in Azure Databricks

  • Train a deep learning model in Azure Databricks
  • Distribute deep learning training by using the Horovod library

Module 8 : Manage machine learning in production with Azure Databricks

  • Automate feature engineering and data pipelines
  • Model development and training
  • Model deployment strategies
  • Model versioning and lifecycle management
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Prerequisites

This learning path assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.