Deep dives into AI engineering, production deployment, MLOps, and modern machine learning practices.
Master production-ready quantization strategies including 8-bit and 4-bit precision, post-training quantization, and hybrid compression workflows. Achieve 2-4x inference speed with 99%+ accuracy recovery on A100/H100 GPUs.
Master prompt caching strategies including cache warming, paged attention, and automatic prefix caching. Learn provider-specific optimizations for OpenAI, Anthropic, and AWS Bedrock to achieve 60-90% cost reduction.
Master production-grade observability for AI agents with OpenTelemetry standards, distributed tracing, and real-time monitoring. Learn session tracing, quality scoring, and debugging autonomous agent decision-making.
Master LLM gateway architecture for production AI systems. Learn multi-provider strategies, cost optimization, security, monitoring, and resilience patterns that enterprises use to manage billions in AI spending.
Master parameter-efficient fine-tuning techniques for production LLMs. Learn when to fine-tune vs. use RAG, implement LoRA and QLoRA, optimize for deployment, and reduce costs by 99% while maintaining performance.
Master production AI evaluation with comprehensive metrics, tools, and strategies. Learn continuous monitoring, drift detection, A/B testing, and hybrid evaluation approaches that improve system quality by 40%.
Master vector databases for production AI systems. Compare Pinecone, Milvus, Qdrant, Weaviate, and Chroma. Learn implementation strategies, optimization techniques, and best practices for RAG, semantic search, and LLM applications.
Master production-ready RAG systems with advanced techniques including hybrid search, GraphRAG, self-reflective RAG, and multimodal retrieval. Learn best practices for building scalable, reliable RAG applications.
Learn how to transform your LLM prototype into a robust, scalable production system. Covers architecture, testing, deployment, and monitoring strategies.
Explore how agentic AI is transforming from simple chatbots to autonomous systems that can perform complex tasks independently. Learn about architecture patterns, frameworks, and production challenges.
Essential MLOps practices for production AI systems. Learn about model monitoring, drift detection, versioning, and continuous improvement strategies.
Practical strategies to dramatically reduce AI infrastructure costs without sacrificing performance. Learn about caching, model optimization, and smart resource management.
A comprehensive guide to taking AI applications from demo to production. Learn about architecture decisions, testing strategies, deployment patterns, and scaling challenges.
