Confidential AI with Ubuntu on Azure - Securing Machine Learning Workloads

Overview

Discover how to protect AI workloads using Ubuntu confidential VMs on Azure, featuring AMD EPYC processors and NVIDIA H100 GPUs for enhanced data privacy and security in cloud-based machine learning.

Syllabus

• Introduction to Confidential AI
Overview of AI workloads
Importance of data privacy and security in AI
• Ubuntu Confidential VMs on Azure
Introduction to Azure’s confidential computing offerings
Features and capabilities of Ubuntu confidential VMs
• Hardware Foundations
Overview of AMD EPYC processors
Leveraging NVIDIA H100 GPUs for AI workloads
Performance and security benefits
• Setting Up the Environment
Provisioning Ubuntu confidential VMs on Azure
Configuring the VMs for machine learning applications
• Secure Data Handling
Data encryption in transit and at rest
Implementing secure data access policies
• Machine Learning on Confidential VMs
Running popular machine learning frameworks
Optimizing AI workloads for confidential computing environments
• Enhancing Security for Machine Learning
Best practices for securing AI models and data
Monitoring and managing security risks
• Case Studies and Practical Applications
Real-world examples of AI workloads using Ubuntu on Azure
Analyzing the impact of confidential computing on AI project success
• Future Trends in Confidential AI
Emerging technologies in AI security
The role of confidential computing in future AI developments
• Conclusion and Next Steps
Summary of key learnings
Resources for further study and exploration of confidential AI systems

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Subjects

Programming