Practical techniques to cut AI model serving latency: from quantization and batching to network, GPU, and scheduler tuning, with checklists and examples.
A practical guide to building robust multi-step AI pipelines with prompt chaining, orchestration patterns, state, testing, guardrails, and cost control.
Understand orchestration vs choreography for APIs: definitions, trade-offs, patterns, tools, and a practical decision guide.
Design and operate a low-latency AI anomaly detection API for streaming data—architecture, models, thresholds, evaluation, and operations.
Build end-to-end observability for AI agents: traces, metrics, logs, and evals to debug, govern privacy, and scale quality, reliability, and cost.
Practical guide to enforcing API governance with standards, automation, and runtime controls—from design reviews to gateways and OPA.
