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.

Lesson

Lab: Create an Azure Machine Learning Workspace

After completing this module, you will be able to

This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

Lesson

Lab: Use Automated Machine Learning

Lab: Use Azure Machine Learning Designer

After completing this module, you will be able to

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.

Lesson

Lab: Run Experiments

Lab: Train Models

After completing this module, you will be able to

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.

Lesson

Lab: Work with Data

After completing this module, you will be able to

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.

Lesson

Lab: Work with Compute

After completing this module, you will be able to

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.

Lesson

Lab: Create a Pipeline

After completing this module, you will be able to

Create pipelines to automate machine learning workflows

Publish and run pipeline services

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.

Lesson

Lab: Create a Real-time Inferencing Service

Lab: Create a Batch Inferencing Service

After completing this module, you will be able to

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.

Lesson

Lab: Tune Hyperparameters

Lab: Use Automated Machine Learning from the SDK

After completing this module, you will be able to

Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.

Lesson

Lab: Explore Differential provacy

Lab: Interpret Models

Lab: Detect and Mitigate Unfairness

After completing this module, you will be able to

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.

Lesson

Lab: Monitor a Model with Application Insights

Lab: Monitor Data Drift

After completing this module, you will be able to