A comprehensive guide to engineering high-performance, scalable, and cost-efficient infrastructure for demanding AI workloads. This course provides advanced strategies for designing systems that support the entire machine learning lifecycle, from large-scale distributed training to low-latency inference serving. You will work with production-grade tools and techniques for resource orchestration, model optimization, and financial governance in complex AI environments.
Prerequisites Python, ML, and Cloud basics
Level:
Distributed Training Systems
Design and implement distributed training jobs using data, model, and pipeline parallelism with frameworks like DeepSpeed and PyTorch FSDP.
Advanced Kubernetes for ML
Orchestrate complex ML workloads on Kubernetes with advanced scheduling for GPUs, spot instances, and multi-tenancy.
Inference Optimization
Architect and deploy high-throughput, low-latency inference services using model compilation, quantization, and specialized serving frameworks.
Scalable Data Systems
Construct scalable feature stores and data processing pipelines for both real-time and batch computation.
AI FinOps
Implement cost management, attribution, and optimization strategies specifically for AI and ML cloud expenditures.
Production MLOps Pipelines
Build automated, end-to-end MLOps pipelines incorporating CI/CD, data versioning, and model monitoring.
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