Target group
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.
Course structure
Module 1: 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.
Lessons
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
Lab : Create an Azure Machine Learning Workspace
- Provision an Azure Machine Learning workspace
- Use tools and code to work with Azure Machine Learning
Module 2: 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.
Lessons
- Automated Machine Learning
- Azure Machine Learning Designer
Lab : Use Automated Machine Learning
Lab : Use Azure Machine Learning Designer
- Use automated machine learning to train a machine learning model
- Use Azure Machine Learning designer to train a model
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 : Train Models
Lab : Run Experiments
- 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 : Work with Data
- Create and use datastores
- Create and use datasets
Module 5: 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.
Lessons
- Working with Environments
- Working with Compute Targets
Lab : Work with Compute
- 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 : Create a Pipeline
- 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
- Continuous Integration and Delivery
Lab : Create a Real-time Inferencing Service
Lab : Create a Batch Inferencing Service
- Publish a model as a real-time inference service
- Publish a model as a batch inference service
- Describe techniques to implement continuous integration and delivery
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 : Use Automated Machine Learning from the SDK
Lab : Tune Hyperparameters
- Optimize hyperparameters for model training
- Use automated machine learning to find the optimal model for your data
Module 9: 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.
Lessons
- Differential Privacy
- Model Interpretability
- Fairness
Lab : Explore Differential provacy
Lab : Interpret Models
Lab : Detect and Mitigate Unfairness
- Apply differential provacy to data analysis
- Use explainers to interpret machine learning models
- Evaluate models for fairness
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 : Monitor Data Drift
Lab : Monitor a Model with Application Insights
- Use Application Insights to monitor a published model
- Monitor data drift
