Course Description
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This Microsoft Azure data science certification 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.
Agenda
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
After completing this module, you will be able to
- Provision an
Azure Machine Learning workspace
- Use tools
and code to work with Azure Machine Learning
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.
Lessons
- Automated
Machine Learning
- Azure
Machine Learning Designer
Lab : Use Automated Machine
Learning
Lab : Use Azure Machine Learning
Designer
After completing this module, you will be able to
- 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 : Run Experiments
Lab : Train Models
After completing this module, you will be able to
- 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
After completing this module, you will be able to
- 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
After completing this module, you will be able to
- 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
After completing this module, you will be able to
- 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
After completing this module, you will be able to
- 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 : Tune Hyperparameters
Lab : Use Automated Machine
Learning from the SDK
After completing this module, you will be able to
- 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 privacy
Lab : Interpret Models
Lab : Detect and Mitigate
Unfairness
After completing this module, you will be able to
- Apply
differential privacy 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 a Model with
Application Insights
Lab : Monitor Data Drift
After completing this module, you will be able to
- Use
Application Insights to monitor a published model
- Monitor data
drift
Audience
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.