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
Lab: Create an Azure Machine Learning Workspace
At the end of this module, the students 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.
Lessons
Lab: Use Automated Machine Learning
Lab: Use Azure Machine Learning Designer
At the end of this module, the students 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.
Lessons
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.
Lessons
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.
Lessons
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.
Lessons
Lab: Create a Pipeline
After completing this module, you will be able to:
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
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.
Lessons
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.
Lessons
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.
Lessons
Lab: Monitor a Model with Application Insights
Lab: Monitor Data Drift
After completing this module, you will be able to: