A practical guide to designing a reproducible benchmarking framework for evaluating AI agents across tasks, tools, and environments.
Build and deploy an AI-powered recommendation system end-to-end: content-based, collaborative filtering, deep retrieval, evaluation, and FastAPI serving.
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.
Design, build, and scale an API-driven data labeling pipeline with quality gates, active learning, and strong governance.
Build a production-ready predictive analytics API with Python and FastAPI—training, serving, security, testing, and MLOps in one tutorial.
