AI Infrastructure, Platforms & Deployment Architecture Training Course

Course Overview

Learning Objectives

• By the end of this training course, participants will be able to:
• Understand the core infrastructure requirements for modern AI workloads.
• Assess compute, storage, networking, and accelerator requirements for different AI applications.
• Design scalable and resilient infrastructure architectures for AI environments.
• Evaluate cloud, on-premises, and hybrid infrastructure options for AI deployment.
• Analyze AI platform architectures and their role in enterprise AI operations.
• Design suitable architectures for model training, inference, and serving.
• Apply containerization and orchestration concepts to AI workloads.
• Develop deployment architectures that support scalability, reliability, and availability.
• Evaluate infrastructure performance, utilization, and cost efficiency.
• Implement monitoring and operational controls across AI infrastructure environments.
• Apply security, governance, and access-control principles to AI platforms.
• Develop an enterprise AI infrastructure and deployment architecture roadmap.

Who Should Attend

Learning Outcomes

• Upon successful completion of the course, participants will be able to:
• Identify infrastructure requirements for AI, machine learning, and generative AI workloads.
• Design compute architectures using appropriate CPUs, GPUs, accelerators, and supporting resources.
• Structure storage and data architectures suitable for AI workloads.
• Design high-performance networking architectures for distributed AI environments.
• Evaluate cloud, on-premises, and hybrid AI infrastructure architectures.
• Select appropriate AI platforms according to workload, performance, security, and operational requirements.
• Design model training, inference, and serving architectures.
• Apply containerization and orchestration approaches to AI deployments.
• Design scalable and highly available AI deployment environments.
• Establish infrastructure monitoring, capacity management, and performance optimization processes.
• Assess AI infrastructure security, governance, and access-control requirements.
• Develop an enterprise-ready AI infrastructure and deployment architecture.

Course Outline