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Implementing a Machine Learning solution with Azure Databricks (DP-3014)

SS Course: GK834020

Course Overview

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Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.

                                                                  

Scheduled Classes

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10/25/24 - GVT - Virtual Classroom - Virtual Instructor-Led
12/20/24 - GVT - Virtual Classroom - Virtual Instructor-Led
02/14/25 - GVT - Virtual Classroom - Virtual Instructor-Led

Outline

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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 : 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

    Prerequisites

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    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.

      Who Should Attend

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      Data scientists and machine learning engineers.