Course Overview

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Key Learning Areas

  • Doing Data Science on Azure
  • Doing Data Science with Azure Machine Learning service
  • Automate Machine Learning with Azure Machine Learning service
  • Manage and Monitor Machine Learning Models with the Azure Machine Learning service

Course Outline

Design a Data Ingestion Strategy for Machine Learning Projects

  • Identify your data source and format
  • Choose how to serve data to machine learning workflows
  • Design a data ingestion solution
  • Lab: Design a data ingestion strategy

Design a Machine Learning Model Training Solution

  • Identify machine learning tasks
  • Choose a service to train a model
  • Choose between compute options
  • Lab: Design a model training strategy

Design a Model Deployment Solution

  • Understand how a model will be consumed
  • Decide whether to deploy your model to a real-time or batch endpoint
  • Lab: Design a deployment solution

Explore Azure Machine Learning Workspace Resources and Assets

  • Create an Azure Machine Learning workspace
  • Identify resources and assets
  • Train models in the workspace
  • Lab: Explore the workspace

Explore Developer Tools for Workspace Interaction

  • The Azure Machine Learning studio
  • The Python Software Development Kit (SDK)
  • The Azure Command Line Interface (CLI)
  • Lab: Explore the developer tools

Make Data Available in Azure Machine Learning

  • Work with Uniform Resource Identifiers (URIs)
  • Create and use datastores
  • Create and use data assets
  • Lab: Make data available

Work with Compute Targets in Azure Machine Learning

  • Choose the appropriate compute target
  • Create and use a compute instance
  • Create and use a compute cluster
  • Lab: Work with compute resources

Work with Environments in Azure Machine Learning

  • Understand environments in Azure Machine Learning
  • Explore and use curated environments
  • Create and use custom environments
  • Lab: Work with environments

Find the Best Classification Model with Automated Machine Learning

  • Prepare your data to use AutoML for classification
  • Configure and run an AutoML experiment
  • Evaluate and compare models
  • Lab: Find the best classification model

Track Model Training in Jupyter Notebooks with MLflow

  • Configure to use MLflow in notebooks
  • Use MLflow for model tracking in notebooks
  • Lab: Track model training

Run a Training Script as a Command Job in Azure Machine Learning

  • Convert a notebook to a script
  • Test scripts in a terminal
  • Run a script as a command job
  • Use parameters in a command job
  • Lab: Run a training script as a command job

Track Model Training with MLflow in Jobs

  • Use MLflow when you run a script as a job
  • Review metrics, parameters, artifacts, and models from a run
  • Lab: Use MLflow to track training jobs

Run Pipelines in Azure Machine Learning

  • Create components
  • Build an Azure Machine Learning pipeline
  • Run an Azure Machine Learning pipeline
  • Lab: Run a pipeline job

Perform Hyperparameter Tuning with Azure Machine Learning

  • Define a hyperparameter search space
  • Configure hyperparameter sampling
  • Select an early-termination policy
  • Run a sweep job
  • Lab: Run a sweep job

Deploy a Model to a Managed Online Endpoint

  • Use managed online endpoints
  • Deploy your MLflow model to a managed online endpoint
  • Deploy a custom model to a managed online endpoint
  • Test online endpoints
  • Lab: Deploy an MLflow model to an online endpoint

Deploy a Model to a Batch Endpoint

  • Create a batch endpoint
  • Deploy your MLflow model to a batch endpoint
  • Deploy a custom model to a batch endpoint
  • Invoke batch endpoints
  • Lab: Deploy an MLflow model to a batch endpoint

Who Benefits

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Prerequisites

Required

  • Creating cloud resources in Microsoft Azure
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow
  • Using Python to explore and visualize data
  • Working with containers

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