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.
Audience profile 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.
Accessing your courseware and registering attendance with Microsoft
To access your Official Curriculum (MOC) course materials you will need a Microsoft.com/Learn account. In Learn you will also be able to register your completion of the event and receive your achievement badge. You will be issued with a unique code during your event.
Before attending this course, students must have:
- A fundamental knowledge of Microsoft Azure.
- Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
- Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
Please note: In order to access the Azure labs for this course you will need to have a Microsoft Outlook account that has/will not be used to associate with any other corporate Azure subscription. You can set up a new Outlook account here
Module 1: Introduction to Azure Machine Learning In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
Lessons Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning Workspace Lab : Working with Azure Machine Learning Tools After completing this module, you will be able to
- Provision an Azure Machine Learning workspace
- Use tools and code to work with Azure Machine Learning
Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
Lessons Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML Designer Lab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to
- Use designer to train a machine learning model
- Deploy a Designer pipeline as a service
Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
Lessons Introduction to Experiments Training and Registering Models Lab : Running Experiments Lab : Training and Registering Models After completing this module, you will be able to
- Run code-based experiments in an Azure Machine Learning workspace
- Train and register machine learning models
Module 4: Working with Data Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Lessons Working with Datastores Working with Datasets Lab : Working with Datastores Lab : Working with Datasets After completing this module, you will be able to
- Create and consume datastores
- Create and consume datasets
Module 5: Compute Contexts One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
Lessons Working with Environments Working with Compute Targets Lab : Working with Environments Lab : Working with Compute Targets After completing this module, you will be able to
- Create and use environments
- Create and use compute targets
Module 6: Orchestrating Operations with Pipelines Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
Lessons Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a Pipeline Lab : Publishing a Pipeline After completing this module, you will be able to
- Create pipelines to automate machine learning workflows
- Publish and run pipeline services
Module 7: Deploying and Consuming Models Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Lessons Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing Service Lab : Creating a Batch Inferencing Service After completing this module, you will be able to
- Publish a model as a real-time inference service
- Publish a model as a batch inference service
Module 8: Training Optimal Models By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Lessons Hyperparameter Tuning Automated Machine Learning Lab : Tuning Hyperparameters Lab : Using Automated Machine Learning After completing this module, you will be able to
- Optimize hyperparameters for model training
- Use automated machine learning to find the optimal model for your data
Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.
Lessons Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning Explanations Lab : Interpreting Models After completing this module, you will be able to
- Generate model explanations with automated machine learning
- Use explainers to interpret machine learning models
Module 10: Monitoring Models After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
Lessons Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application Insights Lab : Monitoring Data Drift After completing this module, you will be able to
- Use Application Insights to monitor a published model
- Monitor data drift
