Build and operate machine learning solutions 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

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Introduction

Understand dataframes

Query dataframes

Visualize data

Exercise - Work with data in Azure Databricks

Knowledge check

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Introduction

Understand machine learning concepts

Perform data cleaning

Perform feature engineering

Perform data scaling

Perform data encoding

Exercise - Prepare data for machine learning

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Introduction

Understand Spark ML

Train and validate a model

Use other machine learning frameworks

Exercise - Train a machine learning model

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Introduction

Understand capabilities of MLflow

Use MLflow terminology

Run experiments

Exercise - Use MLflow to track an experiment

Knowledge check

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Introduction

Describe considerations for model management

Register models

Manage model versioning

Exercise - Manage models in Azure Databricks

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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

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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

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Introduction

Understand hyperparameter tuning

Automated MLflow for model tuning

Hyperparameter tuning with Hyperopt

Exercise

Knowledge check

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Introduction

Understand Horovod

HorovodRunner for distributed deep learning

Exercise

Knowledge check

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