Planning and Optimizing AI Infrastructure
Prerequisites: ML concepts and systems knowledge
Level:
What You'll Learn
Hardware Selection
Evaluate and select appropriate compute hardware, including CPUs, GPUs, and specialized accelerators for different AI workloads.
Infrastructure Design
Design infrastructure solutions for both on-premise and cloud environments based on performance and budget requirements.
Containerization and Orchestration
Use Docker and Kubernetes to create reproducible ML environments and orchestrate distributed training and inference workloads.
Performance Optimization
Apply techniques such as distributed training, mixed-precision, and model quantization to improve the efficiency of AI systems.
Cost Management
Analyze and manage the costs associated with AI infrastructure, implementing strategies to optimize spending in both cloud and on-premise setups.