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.
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.
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. Specifically:
- Creating cloud resources in Microsoft Azure.
- Using Python to explore and visualize data.
- Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
- Working with containers
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
- Getting Started with Azure Machine Learning
- Visual Tools for Machine Learning
- Running Experiments and Training Models
- Working with Data
- Working with Compute
- Orchestrating Operations with Pipelines
- Deploying and Consuming Models
- Training Optimal Models
- Responsible Machine Learning
- Monitoring Models
Getting Started with 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.
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
Lab : Create an Azure Machine Learning Workspace
Visual Tools for Machine Learning
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.
- Automated Machine Learning
- Azure Machine Learning Designer
Lab : Use Automated Machine Learning
Lab : Use Azure Machine Learning Designer
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.
- Introduction to Experiments
- Training and Registering Models
Lab : Train Models
Lab : Run Experiments
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.
- Working with Datastores
- Working with Datasets
Lab : Work with Data
Working with Compute
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.
- Working with Environments
- Working with Compute Targets
Lab : Work with Compute
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.
- Introduction to Pipelines
- Publishing and Running Pipelines
Lab : Create a Pipeline
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.
- Real-time Inferencing
- Batch Inferencing
- Continuous Integration and Delivery
Lab : Create a Real-time Inferencing Service
Lab : Create a Batch Inferencing Service
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.
- Hyperparameter Tuning
- Automated Machine Learning
Lab : Use Automated Machine Learning from the SDK
Lab : Tune Hyperparameters
Responsible Machine Learning
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.
- Differential Privacy
- Model Interpretability
Lab : Explore Differential provacy
Lab : Interpret Models
Lab : Detect and Mitigate Unfairness
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.
- Monitoring Models with Application Insights
- Monitoring Data Drift
Lab : Monitor Data Drift
Lab : Monitor a Model with Application Insights