Build and operate machine learning solutions with Azure Databricks

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Free Online Course: Build and operate machine learning solutions with Azure Databricks provided by Microsoft Learn is a comprehensive online course, which lasts for 4-5 hours worth of material. The course is taught in English and is free of charge.

Overview
    • Module 1: Get started with Azure Databricks
    • After completing this module, you will be able to:

      • Understand Azure Databricks
      • Provision Azure Databricks workspaces and clusters
      • Work with notebooks in Azure Databricks
    • Module 2: Work with data in Azure Databricks
    • After completing this module, you will be able to:

      • Understand dataframes
      • Query dataframes
      • Visualize data
    • Module 3: Prepare data for machine learning with Azure Databricks
    • After completing this module, you will be able to:

      • Understand machine learning concepts
      • Perform data cleaning
      • Perform feature engineering
      • Perform data scaling
      • Perform data encoding
    • Module 4: Train a machine learning model with Azure Databricks
    • After completing this module, you will be able to:

      • Understand Spark ML
      • Train and validate a model
      • Use other machine learning frameworks
    • Module 5: Use MLflow to track experiments in Azure Databricks
    • After completing this module, you will be able to:

      • Understand capabilities of MLflow
      • Use MLflow terminology
      • Run experiments
    • Module 6: Manage machine learning models in Azure Databricks
    • After completing this module, you will be able to:

      • Describe considerations for model management
      • Register models
      • Manage model versioning
    • Module 7: Track Azure Databricks experiments in Azure Machine Learning
    • After completing this module, you will be able to:

      • Describe Azure Machine Learning
      • Run Azure Databricks experiments in Azure Machine Learning
      • Log metrics in Azure Machine Learning with MLflow
      • Run Azure Machine Learning pipelines on Azure Databricks compute
    • Module 8: Deploy Azure Databricks models in Azure Machine Learning
    • After completing this module, you will be able to:

      • Describe considerations for model deployment
      • Plan for Azure Machine Learning deployment endpoints
      • Deploy a model to Azure Machine Learning
      • Troubleshoot model deployment
    • Module 9: Tune hyperparameters with Azure Databricks
    • After completing this module, you will be able to:

      • Understand hyperparameter tuning and its role in machine learning.
      • Learn how to use the two open-source tools - automated MLflow and Hyperopt - to automate the process of model selection and hyperparameter tuning.
    • Module 10: Distributed deep learning with Horovod and Azure Databricks
    • After completing this module, you’ll be able to:

      • Understand what Horovod is and how it can help distribute your deep learning models.
      • Use HorovodRunner in Azure Databricks for distributed deep learning.

Syllabus
    • Module 1: Get started with Azure Databricks
      • Introduction
      • Understand Azure Databricks
      • Provision Azure Databricks workspaces and clusters
      • Work with notebooks in Azure Databricks
      • Exercise - Get started with Azure Databricks
      • Knowledge check
      • Summary
    • Module 2: Work with data in Azure Databricks
      • Introduction
      • Understand dataframes
      • Query dataframes
      • Visualize data
      • Exercise - Work with data in Azure Databricks
      • Knowledge check
      • Summary
    • Module 3: Prepare data for machine learning with Azure Databricks
      • Introduction
      • Understand machine learning concepts
      • Perform data cleaning
      • Perform feature engineering
      • Perform data scaling
      • Perform data encoding
      • Exercise - Prepare data for machine learning
      • Knowledge check
      • Summary
    • Module 4: Train a machine learning model with Azure Databricks
      • Introduction
      • Understand Spark ML
      • Train and validate a model
      • Use other machine learning frameworks
      • Exercise - Train a machine learning model
      • Knowledge check
      • Summary
    • Module 5: Use MLflow to track experiments in Azure Databricks
      • Introduction
      • Understand capabilities of MLflow
      • Use MLflow terminology
      • Run experiments
      • Exercise - Use MLflow to track an experiment
      • Knowledge check
      • Summary
    • Module 6: Manage machine learning models in Azure Databricks
      • Introduction
      • Describe considerations for model management
      • Register models
      • Manage model versioning
      • Exercise - Manage models in Azure Databricks
      • Knowledge check
      • Summary
    • Module 7: Track Azure Databricks experiments in Azure Machine Learning
      • Introduction
      • Describe Azure Machine Learning
      • Run Azure Databricks experiments in Azure Machine Learning
      • Log metrics in Azure Machine Learning with MLflow
      • Run Azure Machine Learning pipelines on Azure Databricks compute
      • Exercise - Use Azure Databricks with Azure Machine Learning
      • Knowledge check
      • Summary
    • Module 8: Deploy Azure Databricks models in Azure Machine Learning
      • Introduction
      • Describe considerations for model deployment
      • Plan for Azure Machine Learning deployment endpoints
      • Deploy a model to Azure Machine Learning
      • Troubleshoot model deployment
      • Exercise - Deploy an Azure Databricks model in Azure Machine Learning
      • Knowledge check
      • Summary
    • Module 9: Tune hyperparameters with Azure Databricks
      • Introduction
      • Understand hyperparameter tuning
      • Automated MLflow for model tuning
      • Hyperparameter tuning with Hyperopt
      • Exercise
      • Knowledge check
      • Summary
    • Module 10: Distributed deep learning with Horovod and Azure Databricks
      • Introduction
      • Understand Horovod
      • HorovodRunner for distributed deep learning
      • Exercise
      • Knowledge check
      • Summary