Multi-Agent AI Systems & Orchestration Patterns
Supervisor patterns, parallel execution, agent-to-agent communication — and when NOT to use them (Day 26)
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Monitoring AI in Production: Drift, Quality & Evals
LLM evaluation frameworks, output monitoring with Langfuse/Helicone, drift detection(Day 25)
AI Cost Optimization: Caching, Batching & Right-sizing
Semantic caching, request batching, model cascading — techniques to slash AI infra bills (Day 24)
Managed AI Services — SageMaker, Vertex AI & Azure ML
When to use managed vs self-hosted, a cloud-by-cloud breakdown for MLOps teams (Day 23)
Deploying AI Models on Kubernetes
A practical field guide to GPU node pools, model serving with vLLM and Triton, and the dark art of autoscaling inference workloads on GKE and EKS — without setting your cloud bill on fire. (Day 22)
MLOps vs LLMOps: What's Different in 2026
Model versioning, prompt versioning, evaluation pipelines — the new ops discipline for AI (Day 21)