Cloud Private

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

1 day
AzureAISpark

Learn how to build, tune, track, and deploy machine learning models at scale with Azure Databricks, Apache Spark, MLflow, AutoML, Optuna, and PyTorch.

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  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
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Course Overview

Azure Databricks is a managed cloud platform for large-scale data analytics and machine learning. Built on Apache Spark, it gives data science, engineering, and analytics teams a unified environment for developing enterprise data and AI solutions.

In this course, you will learn to prepare data, train and evaluate machine learning models, track experiments, optimize hyperparameters, use AutoML and deep learning, and manage model deployment and lifecycle processes. The course is designed for aspiring data scientists and AI engineers who want to build and manage machine learning solutions with Azure Databricks.

Course Benefits

  • Navigate Azure Databricks and identify its primary data analytics and machine learning workloads.
  • Use Apache Spark clusters and notebooks to process, analyze, and visualize data.
  • Prepare data and train and evaluate machine learning models in Azure Databricks.
  • Use MLflow to run experiments and register and serve models.
  • Optimize model hyperparameters with Optuna and scale optimization workloads.
  • Build machine learning models with Azure Databricks AutoML through the user interface and code.
  • Train deep learning models with PyTorch and distribute training with TorchDistributor.
  • Automate data transformations and apply model deployment, versioning, and lifecycle-management strategies.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Microsoft Certified Partner

Course Outline

  1. Explore Azure Databricks
    1. Get started with Azure Databricks
    2. Identify Azure Databricks workloads
    3. Understand key concepts
    4. Explore data governance with Unity Catalog and Microsoft Purview
  2. Use Apache Spark in Azure Databricks
    1. Get to know Spark
    2. Create a Spark cluster
    3. Use Spark in notebooks
    4. Use Spark to work with data files
    5. Visualize data
  3. Train a Machine Learning Model in Azure Databricks
    1. Understand machine learning principles
    2. Explore machine learning in Azure Databricks
    3. Prepare data for machine learning
    4. Train a machine learning model
    5. Evaluate a machine learning model
  4. Use MLflow in Azure Databricks
    1. Explore MLflow capabilities
    2. Run experiments with MLflow
    3. Register and serve models with MLflow
  5. Tune Hyperparameters in Azure Databricks
    1. Optimize hyperparameters with Optuna
    2. Review trials
    3. Scale hyperparameter optimization
  6. Use AutoML in Azure Databricks
    1. Explore AutoML
    2. Use AutoML in the Azure Databricks user interface
    3. Run an AutoML experiment with code
  7. Train Deep Learning Models in Azure Databricks
    1. Understand deep learning concepts
    2. Train models with PyTorch
    3. Distribute PyTorch training with TorchDistributor
  8. Manage Machine Learning in Production with Azure Databricks
    1. Automate data transformations
    2. Explore model development
    3. Explore model deployment strategies
    4. Explore model versioning and lifecycle management

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Azure class:

  • Experience using Python to explore data and train machine learning models.
  • Fundamental knowledge of data analytics and the machine learning model-training process.
  • Experience with at least one common open-source machine learning framework, such as Scikit-Learn, PyTorch, or TensorFlow.

Experience in the following would be useful for this Azure class:

  • Basic familiarity with Azure Databricks is helpful but not required.
  • Prior study of Azure data fundamentals and introductory machine learning model development may be beneficial.

Have questions about this course?

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