DP-3014 Implementing a Machine Learning solution with Azure Databricks

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Course Overview

Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.

Course Outline

1 - Explore Azure Databricks

  • Get started with Azure Databricks
  • Identify Azure Databricks workloads
  • Understand key concepts

2 - Use Apache Spark in Azure Databricks

  • Get to know Spark
  • Create a Spark cluster
  • Use Spark in notebooks
  • Use Spark to work with data files
  • Visualize data

3 - Train a machine learning model in Azure Databricks

  • Understand principles of machine learning
  • Machine learning in Azure Databricks
  • Prepare data for machine learning
  • Train a machine learning model
  • Evaluate a machine learning model

4 - Use MLflow in Azure Databricks

  • Capabilities of MLflow
  • Run experiments with MLflow
  • Register and serve models with MLflow

5 - Tune hyperparameters in Azure Databricks

  • Optimize hyperparameters with Hyperopt
  • Review Hyperopt trials
  • Scale Hyperopt trials

6 - Use AutoML in Azure Databricks

  • What is AutoML?
  • Use AutoML in the Azure Databricks user interface
  • Use code to run an AutoML experiment

7 - Train deep learning models in Azure Databricks

  • Understand deep learning concepts
  • Train models with PyTorch
  • Distribute PyTorch training with Horovod

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Class Dates & Times

Class times are listed Eastern time

This is a 1-day class

Register for Class

Register When Time Where How
Register 05/30/2025 9:00AM - 5:00PM Online VILT
Register 09/02/2025 9:00AM - 5:00PM Online VILT
Register 11/26/2025 9:00AM - 5:00PM Online VILT

The classes listed are available to all United Training customers and does not reflect, in any way,
the availability or support of technology within the University of Maine System.