LLMs in Production: From language models to successful products

Description

LLMs in Production: From language models to successful products.

Take Generative AI from experimentation to production with a practical guide to deploying, optimizing, and scaling Large Language Models.

Building powerful AI models is only the beginning. Successfully running them in production requires the right architecture, deployment strategies, monitoring, and optimization techniques. This hands-on guide equips machine learning engineers, data scientists, AI developers, and MLOps professionals with the knowledge needed to build reliable, efficient, and scalable LLM-powered applications.

Through practical examples and real-world implementations, you’ll explore modern techniques such as Retrieval-Augmented Generation (RAG), vector databases, parameter-efficient fine-tuning, scalable inference, and production-ready AI workflows. Learn how to reduce infrastructure costs, improve model performance, and create robust AI systems capable of serving real users at scale.

Inside You’ll Learn How To:

• Build a solid understanding of Large Language Models, including tokenization, transformer architectures, and the evolution of foundation models.
• Enhance model accuracy using Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
• Compare full model training with modern fine-tuning techniques such as PEFT, LoRA, QLoRA, knowledge distillation, and Reinforcement Learning from Human Feedback (RLHF).
• Design effective prompts, structured outputs, AI agents, and multi-step workflows that go beyond simple prompt engineering.
• Deploy LLMs across cloud platforms, Kubernetes environments, edge devices, and commodity hardware while optimizing performance and infrastructure costs.
• Improve AI safety with guardrails for hallucination reduction, adversarial testing, compliance, and responsible AI practices.
• Implement modern LLMOps workflows for model deployment, monitoring, automated retraining, versioning, and continuous evaluation.

Hands-On Projects Include:

• Training and customizing a domain-specific Large Language Model from the ground up.
• Developing an AI-powered Visual Studio Code extension that delivers intelligent coding assistance and productivity features.
• Deploying lightweight language models on edge hardware such as Raspberry Pi and NVIDIA Jetson devices for real-world embedded AI applications.

Whether you’re bringing your first LLM into production or refining an enterprise-scale AI platform, this guide provides the practical techniques, proven architectures, and engineering best practices needed to build dependable, high-performance Generative AI solutions.