LLMs in Production: Engineering AI Applications

Unlock the potential of Generative AI with this Large Language Model production-ready playbook for seamless deployment, optimization, and scaling. This hands-on guide takes you beyond theory, offering expert strategies for integrating LLMs into real-world applications using retrieval-augmented generation (RAG), vector databases, PEFT, LoRA, and scalable inference architectures. Whether you're an ML engineer, data scientist, or MLOps practitioner, you’ll gain the technical know-how to operationalize LLMs efficiently, reduce compute costs, and ensure rock-solid reliability in production.

What You’ll

Master LLM Fundamentals – Understand tokenization, transformer architectures, and the evolution linguistics to the creation of foundation models. RAG & Vector Databases – Augment model capabilities with real-time retrieval and memory-optimized embeddings. Training vs Fine-tuning – Learn how to train your own model as well as cutting edge techniques like Distillation, RLHF, PEFT, LoRA, and QLoRA for cost-effective adaptation. Prompt Engineering – Discover the quickly evolving world of prompt engineering and go beyond simple prompt and pray methods and learn how to implement structured outputs, complex workflows, and LLM agents. Scaling & Cost Optimization – Deploy LLMs into your favorite cloud of choice, on commodity hardware, Kubernetes clusters, and edge devices. Securing AI Workflows – Implement guardrails for hallucination mitigation, adversarial testing, and compliance monitoring. MLOps for LLMs – Learn all about LLMOps, automate model lifecycle management, retraining pipelines, and continuous evaluation. Hands-on Projects

Training a custom LLM from scratch – Build and optimize an industry-specific model.

AI-Powered VSCode Extension – Use LLMs to enhance developer productivity with intelligent code completion.

Deploying on Edge Devices – Run a lightweight LLM on a Raspberry Pi or Jetson Nano for real-world AI applications.

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