Description

• 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.