Complete MLOps guide for 2026: model versioning with MLflow, containerization, serving with FastAPI and Triton, monitoring, A/B testing, and CI/CD pipelines for ML models. Production patterns from top ML teams.
Master PM2 for Node.js production in 2026: cluster mode, zero-downtime deploys, monitoring, log management, startup scripts, ecosystem configuration, and health monitoring.
Build a comprehensive analytics backend for AI features. Track queries, user satisfaction, funnel conversion, and detect anomalies in AI system behavior.
Guide to building domain-specific LLM benchmarks, task-based evaluation, adversarial testing, and detecting benchmark contamination for production use cases.
Learn how to use feature flags to safely roll out LLM features, implement percentage-based rollouts, and build kill switches for AI-powered capabilities.
Learn when to route requests to humans, design review queues, and use human feedback to improve AI systems. Build human-in-the-loop workflows that scale.
Comprehensive guide to evaluating LLM performance in production using offline metrics, online evaluation, human sampling, pairwise comparisons, and continuous monitoring pipelines.
Implement per-user token budgets, tiered model access, request queuing, cost attribution, real-time dashboards, and anomaly detection to prevent AI bill shock.
Deep dive into Bun''s production readiness, benchmarks against Node.js, and practical migration strategies with real compatibility gaps and when to migrate.
Fine-tune embeddings for specialized domains. Generate training pairs with LLMs, train with sentence-transformers, and deploy custom embedding models in production.
Master advanced LLM chaining patterns including sequential, parallel, conditional, and map-reduce chains. Learn to orchestrate complex AI workflows in production.
Manage long conversations and large documents within LLM context limits using sliding windows, summarization, and map-reduce patterns to avoid the lost-in-the-middle problem.
Master system prompt architecture, persona design, and context management for production LLM applications. Learn structured prompt patterns that improve consistency and quality.
Master function calling with schema design, parallel execution, error handling, and recursive loops to build autonomous LLM agents that work reliably at scale.
Learn how to integrate LLM calls into microservice architectures with async patterns, job queues, and service contracts that don''t introduce latency bottlenecks.
Route queries intelligently to cheaper or more capable models based on complexity, intent, and latency SLAs, saving 50%+ on LLM costs while maintaining quality.
Master end-to-end LLM observability with OpenTelemetry spans, Langfuse tracing, and token-level cost tracking to catch production issues before users do.
Comprehensive architecture for production LLM systems covering request pipelines, async patterns, cost/latency optimization, multi-tenancy, observability, and scaling to 10K concurrent users.
Extract reliable structured data from LLMs using JSON mode, Zod validation, and intelligent retry logic to eliminate parsing failures and hallucinations.
Master LLM token economics by implementing token counting, setting budgets, and optimizing costs across your AI infrastructure with tiktoken and practical middleware patterns.
Deploy Pinecone at scale with namespaces for multi-tenancy, metadata filtering strategies, batch operations, hybrid search, and cost optimization tactics.
Explore naive RAG limitations and advanced architectures like modular RAG, self-RAG, and corrective RAG that enable production-grade question-answering systems.
Build comprehensive monitoring for RAG systems tracking retrieval quality, generation speed, user feedback, and cost metrics to detect quality drift in production.
Implement zero-downtime secrets rotation with AWS Secrets Manager, blue/green secret versions, and automated password rotation for PostgreSQL and API keys.