AI Infrastructure & MLOps
Build and maintain the infrastructure that keeps AI running in production. This path covers model serving, GPU cluster management, ML pipelines, monitoring, CI/CD for machine learning, cost optimization, and the operational practices that make AI reliable at scale.
What You'll Learn
- Design scalable model serving infrastructure
- Manage GPU clusters for training and inference
- Build ML pipelines with proper CI/CD
- Implement model monitoring and observability
- Optimize AI infrastructure costs
- Handle model versioning and rollback
- Set up evaluation and testing for ML systems
Course Lessons
MLOps Fundamentals: Why AI Infrastructure Is Different
18 min readUnderstand why ML systems need specialized infrastructure and operations — data dependencies, model versioning, and the unique failure modes of AI applications.
Model Serving at Scale
25 min readDeploy models using vLLM, TGI, Triton, or managed services. Handle batching, caching, auto-scaling, and load balancing for inference workloads.
GPU Cluster Management
22 min readManage GPU resources for training and inference — scheduling, multi-tenancy, spot instances, and optimizing GPU utilization rates.
Read lesson →ML Pipelines and CI/CD
22 min readBuild automated ML pipelines that handle data processing, training, evaluation, and deployment with proper version control and reproducibility.
Model Monitoring and Observability
20 min readMonitor AI systems in production — performance degradation, data drift, output quality, and the specialized metrics that matter for ML.
Cost Optimization for AI Infrastructure
18 min readReduce AI infrastructure costs through quantization, distillation, caching, spot instances, and intelligent routing between model sizes.
Incident Response and Model Rollback
15 min readHandle AI incidents — detecting problems, rolling back models safely, root cause analysis, and preventing regression in ML systems.
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Put Your Learning into Practice
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