Exploring the Inner Workings of Azure's Advanced AI Infrastructure

Overview

Delve into Azure's advanced AI infrastructure, exploring GPU architectures, networking technologies, and system designs powering modern AI model training and deployment at scale.

Syllabus

• Introduction to Azure’s AI Infrastructure
Overview of Azure's AI capabilities
Importance and impact of AI infrastructure on modern applications
• GPU Architectures in Azure
Understanding GPU roles in AI processing
Overview of Azure's GPU offerings
Comparison of different GPU architectures (e.g., NVIDIA's Ampere vs. Volta)
• Networking Technologies in Azure
High-performance networking requirements for AI
Azure's network optimizations for AI workloads
Introduction to Infiniband and RDMA in Azure
• System Designs for AI Model Training
Scaling model training on Azure
Distributed training strategies
Role of Azure Machine Learning in model training
• Deployment Strategies for AI Models
Overview of Azure AI deployment options
Utilizing Azure Kubernetes Service (AKS) for AI
Managing and updating AI models in production
• Case Studies and Real-World Applications
Review of successful AI implementations on Azure
Industry-specific AI applications and customizations
• Best Practices and Optimization
Cost optimization strategies for AI workloads on Azure
Performance tuning and resource allocation
Security considerations for AI infrastructure
• Future Trends in Azure AI Infrastructure
Emerging technologies and their impact on AI
Azure's roadmap for AI infrastructure developments
• Hands-on Labs and Workshops
Practical exercises on setting up AI environments
Hands-on model training and deployment tasks
Collaborative projects to reinforce learning
• Course Conclusion
Recap of key concepts
Resources for further learning and exploration in Azure AI solutions

Subjects

Programming