Design and operate a low-latency AI anomaly detection API for streaming data—architecture, models, thresholds, evaluation, and operations.
A practical guide to designing an AI churn prediction API—architecture, data/modeling choices, endpoints, MLOps, metrics, and code examples.
Build end-to-end observability for AI agents: traces, metrics, logs, and evals to debug, govern privacy, and scale quality, reliability, and cost.
Design, build, and scale an API-driven data labeling pipeline with quality gates, active learning, and strong governance.
A practical guide to integrating AI-powered weather prediction APIs with code, architecture, and MLOps best practices for reliable forecasts.
A practical guide to selecting, optimizing, and operating small language models for edge deployment—latency, memory, tooling, and MLOps.
