Azure-Powered Machine Learning Security

Day 1: Foundations of Machine Learning Security

Topics Covered:

• Introduction to machine learning (ML) security challenges and threats.
• Overview of the Zero Trust security model in AI workflows.
• Fundamentals of securing cloud-based ML systems in Microsoft Azure.
• Implementing secure authentication and authorization with Azure Active Directory (AAD).
• Role-based access control (RBAC) essentials for safeguarding resources.

Learning Objectives:

• Understand the critical risks and vulnerabilities in machine learning environments.
• Apply the principles of the Zero Trust model to secure AI workflows.
• Configure Azure Active Directory for robust identity and access management.
• Utilize RBAC to assign and manage permissions effectively.

Day 2: Securing Machine Learning Operations

Topics Covered:

• Protecting ML models and data during the training and deployment phases.
• Managing secure data storage and compute environments in Azure.
• Exploring Azure Key Vault for managing sensitive information like secrets, keys, and certificates.
• Introduction to encryption techniques for data-in-transit and at-rest.

Learning Objectives:

• Secure ML pipelines by safeguarding data and compute resources.
• Implement Azure Key Vault to enhance data security and manage credentials.
• Utilize encryption to protect sensitive ML assets.
• Optimize and monitor secure data access across workflows.

Day 3: Automating Compliance and Workflow Security

Topics Covered:

• Building secure, event-driven workflows using Azure Event Grid and Logic Apps.
• Automating threat detection and response in ML environments.
• Integrating compliance automation tools to meet regulatory requirements.
• Hands-on labs: Configuring automated alerts and logging in Azure.

Learning Objectives:

• Design and deploy secure event-driven ML workflows.
• Automate compliance monitoring using Azure tools and services.
• Implement proactive security measures for AI/ML pipelines.
• Develop event-driven responses to threats with Azure Event Grid.

Day 4: Ethical and Responsible AI Practices

Topics Covered:

• Introduction to Responsible AI and ethical considerations in ML projects.
• Using Azure’s fairness tools, interpretability features, and compliance dashboards.
• Monitoring deployed ML models for fairness, bias, and accuracy.
• Final hands-on labs: End-to-end project integrating security and Responsible AI.

Learning Objectives:

• Apply Responsible AI principles to ensure fairness and ethical use of ML systems.
• Leverage Azure’s tools to assess and improve model transparency and bias mitigation.
• Monitor deployed ML solutions for compliance and ethical performance.
• Integrate security, compliance, and ethics in real-world AI projects.

Instructor Kit

(PPTX/PDF of Slides + Optional Instructor Notes)
Comprehensive slide deck with detailed content covering all modules, plus optional instructor notes to enhance teaching effectiveness.

Student Kit / Handout

(with Free Branding)
Professionally designed handouts for students, including all essential course information and customizable branding options for your organization.

Course Agenda / Outline

Detailed day-by-day course agenda and outline, ensuring smooth course delivery and a structured learning experience for students.

Study Guide

A concise guide summarizing key concepts and topics covered in the course, perfect for post-course review and exam preparation.

FAQ

Answers to commonly asked questions about the course content, delivery, and labs to support instructors and students.

Briefing Doc

A high-level document summarizing the course objectives, target audience, and key learning outcomes, ideal for internal use and marketing.

Sales Enablement Kit for IT Training Sales Engineers

(Additional Fee)
Exclusive toolkit designed for IT training sales teams, including pitch decks, objection handling, and ROI documentation to support course sales.

Course AI GPT

(Course Assistant GPT so students can talk to the course materials!)
A cutting-edge AI-driven assistant that allows students to interact with course content, ask questions, and receive instant feedback.

Optional Podcast

(of the entire course or for each individual module)
Engaging audio content covering the entire course or individual modules, perfect for on-the-go learning or reinforcement.

Lab Guide

(Lab Environments are additional and can be found at CourseLabs.io)
Step-by-step lab guide to support hands-on learning, with lab environments available separately at CourseLabs.io.

Lab Files

(If you choose to host your own lab environment)
All necessary files and instructions for setting up and running labs in your own environment, offering flexibility in deployment.

Microsoft Azure Machine Learning: Latest stable version

Visual Studio Code: Latest stable version with Azure ML extensions

Azure CLI & PowerShell: Latest stable version

Fairlearn SDK: Preview version

Azure Kubernetes Service (AKS): Latest stable version

Azure DevOps (Boards, Repos, Pipelines, Test Plans, Artifacts): Latest stable version

Terraform on Azure: Latest stable version

Azure Bicep: Latest stable version

Azure Key Vault: Latest stable version

Azure Event Grid: Latest stable version

Jupyter Notebooks: Latest stable version

This dynamic course combines the best of theoretical learning and practical application to empower students with a comprehensive understanding of machine learning security in Azure. With a balanced format of 50% lecture and 50% hands-on labs, participants will gain real-world experience to secure and manage AI workflows effectively.

Course Objectives

• Understand the principles of securing machine learning workflows using Microsoft Azure.
• Implement role-based access control (RBAC) and ensure adherence to the Zero Trust model.
• Automate compliance and streamline processes with Azure Event Grid and Logic Apps.
• Utilize Azure’s Responsible AI tools to design ethical and compliant AI solutions.
• Explore real-world scenarios to configure, secure, and optimize ML environments.

Learning Objectives

By the end of this course, students will be able to:

• Deploy and secure machine learning models on Azure.
• Apply robust security measures to protect data and workflows.
• Automate operations using event-driven workflows in Azure.
• Leverage Azure tools to monitor, manage, and secure AI solutions.

Who Should Attend

This course is designed for:

• AI and ML professionals aiming to enhance their expertise in secure ML practices.
• IT security specialists seeking to integrate AI workflows securely.
• Data scientists and engineers looking to implement secure, ethical AI.
• Organizations adopting machine learning solutions and needing compliant AI systems.

Flexible Training Options

We understand that every organization has unique training needs. That’s why our courseware can be fully customized into 1, 2, 3, 4, or 5-day formats, allowing for maximum flexibility. The 5-day course is priced at $40 per student per day, ensuring an affordable and scalable training solution.