Azure ML & Data Science Workshop
Instructor-Led Training / Workshop / Data & AI
This workshop spans over three days of learning based on artifacts like presentations, hands-on labs, and interactive activities. The three days are structured as follows:
Day 1 is focused on Data Science and Azure Machine Learning fundamentals. It starts with an architectural overview of Azure Machine Learning followed by a demo of the Azure Machine Learning workspace. Next, it touches on the Data Science process and the organization and inner workings of a Data Science team. Following the Data Science topics, the day covers two of Azure Machine Learning’s core infrastructure aspects: compute resources and security. The day also touches on the critical aspects of responsible Artificial Intelligence and Machine Learning.
Day 2 is focused on Azure Machine Learning development. It starts with the topic of no-code development followed by a discussion on Automated ML. Next, it covers the two main approaches used in code-first development: the Azure Machine Learning SDK for Python and the Azure CLI extension for Azure Machine Learning. Both presentations on these topics are doubled by practical exercises in the form of hands-on labs.
Day 3 is focused on Azure Machine Learning integration tasks ranging from data integration and data processes to Machine Learning operations (MLOps). The day starts addressing two of the core data tasks in Machine Learning: privacy protection and drift monitoring. Next, the integration scenarios with Azure Synapse Analytics and Azure Databricks are covered. MLOps is also discussed and practiced via a hands-on lab. The day ends with a high-level interactive discussion on how Azure is being used in modern data science projects.
The workshop is addressed to the following technical audience:
- Data Scientists
- Data Engineers
- Machine Learning developers
- Machine Learning engineers
- DevOps, DevSecOps, MLOps, or AIOps engineers
This is a Level-400 workshop, which means it focuses on medium to advanced topics and does not introduce basic concepts and notions. To be proficient in the workshop activities, attendees are expected to have the following pre-requisite knowledge:
|Machine Learning concepts||Basic to Medium understanding of Machine Learning concepts and processes.|
|Azure Machine Learning service||Basic to Medium understanding of service components.|
|Azure Machine Learning development||Basic to Medium understanding of the Azure Machine Learning SDK for Python.|
|Programming in Python||Basic to Medium understanding of Python programming concepts.|
|Azure CLI||Basic understanding of Azure CLI concepts and commands execution.|
|Azure Synapse Analytics||Basic experience with Azure Synapse Studio and workspaces.|
|Azure Databricks||Basic experience with Azure Databricks workspaces and notebooks.|
|Azure DevOps||Basic to Medium understanding of Azure DevOps (including Pipelines and GitHub integration).|
|GitHub||Basic to Medium understanding of GitHub (including Actions).|
- Azure Machine Learning (AML) Overview & Architecture
- Introducing the Data Science process
- Organizing and Working in a Data Science Team
- Using Compute Resources in AML
- Training and deploying a model in AML
- Responsible AI and Machine Learning
- Securing an AML workspace
- Establishing a security baseline for AML
- Azure Machine Learning no-code development
- Implementing ML pipelines with the designer
- Automated ML
- Use cases for AutoML
- Azure ML SDK for Python
- Implementing and monitoring a batch scoring solution
- Azure CLI extension for AML
- Deploying a real-time scoring endpoint with managed compute
- Privacy-enhancing Machine Learning with sensitive data
- Data preparation and monitoring
- AML integration with Azure Synapse Analytics
- Using AML from Synapse Analytics Studio
- AML integration with Azure Databricks
- MLOps best practices
- MLOps with AML, Azure Pipelines, and GitHub
- Addressing the challenges of modern data science projects with Azure
Ready to Get Started?
Our Training Team is ready to help you get started on your training journey. Reach out and let us know your training needs so we can discuss how best we can help.
Trained by experts
Our instructors are expert practitioners who share their real-world experience and insights into the topics covered by this course in a pragmatic way.
We offer a safe environment where you can experiment with realistic data and ask questions throughout the workshop.
This course includes fully-functioning lab environments. All you need to access the lab environments is a modern web browser.