Advanced Data Science Operations: Optimizing Natural Language Processing with LLM Workloads in Python

Running an LLM in production is only the beginning. Keeping it reliable, secure, fast, and cost-effective is the real challenge. Advanced Data Science Operations: Optimizing Natural Language Processing with LLM Workloads in Python provides a practical roadmap for engineers, data scientists, and AI teams moving from experimental NLP and generative AI projects to dependable production systems. Explore the operational side of modern LLMOps and AI infrastructure-from preparing large-scale data and managing embeddings to fine-tuning models, optimizing RAG pipelines, evaluating generated responses, and controlling inference costs. Inside, you'll learn how to approach: - LLM fine-tuning with PEFT, LoRA, and QLoRA- Production RAG operations, vector databases, semantic search, and retrieval quality- LLM inference optimization using batching, caching, vLLM, and TensorRT-LLM- AI evaluation and monitoring for model quality, hallucinations, and regressions- Security and guardrails, including prompt injection defense and PII protection- LLM cost management, provider routing, quotas, and self-hosting strategies- Agentic AI operations, tool calling, tracing, and runaway-agent debugging- CI/CD and enterprise LLMOps for scalable, sustainable AI workflowsWhether you're building NLP applications, operating generative AI infrastructure, or preparing LLM workloads for enterprise scale, this book helps you think beyond the model and focus on the systems that make AI work reliably in production. Turn Python-based LLM experiments into operationally disciplined AI systems. Start building your LLMOps foundation today.