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Implementing a Machine Learning Solution with Azure Databricks

  • CMDBID 1000918
  • Course Code MDP3014
  • Duration 1 Days
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Microsoft Data Course

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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.

Audience

This course is designed for data scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks

Skills Gained

  • Provision an Azure Databricks workspace and cluster
  • Use Azure Databricks to train a machine learning model
  • Use MLflow to track experiments and manage machine learning models
  • Integrate Azure Databricks with Azure Machine Learning

Prerequisites

This course 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.

Outline

Module 1: Explore Azure Databricks
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
  • Introduction
  • Get started with Azure Databricks
  • Identify Azure Databricks workloads
  • Understand key concepts
  • Exercise - Explore Azure Databricks
  • Knowledge check
  • Summary
Module 2: Use Apache Spark in Azure Databricks
Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
  • Introduction
  • Get to know Spark
  • Create a Spark cluster
  • Use Spark in notebooks
  • Use Spark to work with data files
  • Visualize data
  • Exercise - Use Spark in Azure Databricks
  • Knowledge check
  • Summary
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.
  • Introduction
  • 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
  • Exercise - Train a machine learning model in Azure Databricks
  • Knowledge check
  • Summary
Module 4: Use MLflow in Azure Databricks
MLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.
  • Introduction
  • Capabilities of MLflow
  • Run experiments with MLflow
  • Register and serve models with MLflow
  • Exercise - Use MLflow in Azure Databricks
  • Knowledge check
  • Summary
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.
  • Introduction
  • Optimize hyperparameters with Hyperopt
  • Review Hyperopt trials
  • Scale Hyperopt trials
  • Exercise - Optimize hyperparameters for machine learning in Azure Databricks
  • Knowledge check
  • Summary
Module 6: Use AutoML in Azure Databricks
AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.
  • Introduction
  • What is AutoML?
  • Use AutoML in the Azure Databricks user interface
  • Use code to run an AutoML experiment
  • Exercise - Use AutoML in Azure Databricks
  • Knowledge check
  • Summary
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.
  • Introduction
  • Understand deep learning concepts
  • Train models with PyTorch
  • Distribute PyTorch training with Horovod
  • Exercise - Train deep learning models on Azure Databricks
  • Knowledge check
  • Summary

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