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

By Axel Sundvall

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 QLoRAProduction RAG operations, vector databases, semantic search, and retrieval qualityLLM inference optimization using batching, caching, vLLM, and TensorRT-LLMAI evaluation and monitoring for model quality, hallucinations, and regressionsSecurity and guardrails, including prompt injection defense and PII protectionLLM cost management, provider routing, quotas, and self-hosting strategiesAgentic AI operations, tool calling, tracing, and runaway-agent debuggingCI/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.

• Format: Paperback
• Language: English
• ISBN: B0HJC3KXZD
• ISBN13: 9798172802560
• Release Date: September 2026
• Publisher: Independently Published
• Length: 172 Pages
• Weight: 0.68 lbs.
• Dimensions: 0.4" x 7.0" x 10.0"