Implement a machine learning solution 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

After completing this module, you will be able to:

• Understand dataframes
• Query dataframes
• Visualize data

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

After completing this module, you will be able to:

• Understand Spark ML
• Train and validate a model
• Use other machine learning frameworks

After completing this module, you will be able to:

• Understand capabilities of MLflow
• Use MLflow terminology
• Run experiments

After completing this module, you will be able to:

• Describe considerations for model management
• Register models
• Manage model versioning

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

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

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.

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.

• 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

• Introduction
• Understand dataframes
• Query dataframes
• Visualize data
• Exercise - Work with data in Azure Databricks
• Knowledge check
• Summary

• 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

• Introduction
• Understand Spark ML
• Train and validate a model
• Use other machine learning frameworks
• Exercise - Train a machine learning model
• Knowledge check
• Summary

• Introduction
• Understand capabilities of MLflow
• Use MLflow terminology
• Run experiments
• Exercise - Use MLflow to track an experiment
• Knowledge check
• Summary

• Introduction
• Describe considerations for model management
• Register models
• Manage model versioning
• Exercise - Manage models in Azure Databricks
• Knowledge check
• Summary

• 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

• 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

• Introduction
• Understand hyperparameter tuning
• Automated MLflow for model tuning
• Hyperparameter tuning with Hyperopt
• Exercise
• Knowledge check
• Summary

• Introduction
• Understand Horovod
• HorovodRunner for distributed deep learning
• Exercise
• Knowledge check
• Summary