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.
Module 2: No-Code 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.
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.
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.
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.
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.
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.
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.
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.
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.
