Machine Learning Mastery

Continuous batching resolves static batching’s padding-induced GPU idleness by enabling dynamic scheduling and ragged batching, significantly improving throughput and latency in multi-user LLM inference—real-world tests show 2–3x throughput gains and up to 50% lower average latency.

LLMOps is the engineering practice for building production-grade large language model systems, covering observability, evaluation, cost control, and agent orchestration by treating LLM systems as versioned, monitored, and iteratively improvable software.

The article explains three levels of Agentic RAG, contrasts its limitations with traditional RAG, and introduces how agent mechanisms improve information retrieval and generation.

Agentic programming is a paradigm where AI models act as autonomous decision engines inside software systems—executing workflows rather than just responses—yet only 11% of enterprises run agents in production, mainly due to engineering and architectural gaps, not lack of demand.

The article proposes using prompt compression to reduce agentic loop costs, providing specific implementation methods and experimental data.

The article introduces how to implement permission-gated tool calling in Python agent systems, providing specific code examples and security strategies.