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{ "item_title" : "Ollama & Local AI", "item_author" : [" Max Reedwell "], "item_description" : "Frustrated by the high costs, slow latency, and data privacy risks of proprietary cloud LLM APIs? This book is the definitive, hands-on guide for AI developers, DevOps engineers, and technical leaders who are ready to take full control of their AI stack. Ollama & Local AI provides a practical, code-driven roadmap to self-hosting, fine-tuning, and deploying powerful open-source models like Llama and Mistral directly on your own hardware. Move beyond simple API consumption, gain absolute data sovereignty, and dramatically reduce your inference costs.This is not a high-level overview; it's a complete production playbook. Inside, you will find the precise, step-by-step instructions to:Master Installation: Set up and manage complete Ollama and LocalAI ecosystems, from simple scripts to production-ready Docker and Kubernetes deployments.Fine-Tune Custom Models: Learn to perform efficient LoRA and QLoRA fine-tuning using modern tools like Unsloth and Axolotl to create models with specialized skills.Optimize and Deploy: Convert, quantize, and merge models into the high-performance GGUF format using llama.cpp workflows for deployment in both Ollama and LocalAI.Build Secure APIs: Architect secure, high-throughput REST APIs for your models using an Nginx reverse proxy for enterprise-grade authentication.Orchestrate Workflows: Integrate your local models into complex LangChain pipelines to build powerful applications like Retrieval-Augmented Generation (RAG).Troubleshoot Like a Pro: Diagnose and solve common pitfalls in VRAM management, CUDA conflicts, and performance bottlenecks.Stop renting your AI. Build, deploy, and own your high-performance LLM infrastructure today.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/827/363/9798273638556_b.jpg", "price_data" : { "retail_price" : "19.99", "online_price" : "19.99", "our_price" : "19.99", "club_price" : "19.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Ollama & Local AI|Max Reedwell

Ollama & Local AI : A Practical Guide to Self-Hosting, Fine-Tuning, and Deploying Open-Source LLMs for Production

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Overview

Frustrated by the high costs, slow latency, and data privacy risks of proprietary cloud LLM APIs? This book is the definitive, hands-on guide for AI developers, DevOps engineers, and technical leaders who are ready to take full control of their AI stack. Ollama & Local AI provides a practical, code-driven roadmap to self-hosting, fine-tuning, and deploying powerful open-source models like Llama and Mistral directly on your own hardware. Move beyond simple API consumption, gain absolute data sovereignty, and dramatically reduce your inference costs.
This is not a high-level overview; it's a complete production playbook. Inside, you will find the precise, step-by-step instructions to:
Master Installation: Set up and manage complete Ollama and LocalAI ecosystems, from simple scripts to production-ready Docker and Kubernetes deployments.
Fine-Tune Custom Models: Learn to perform efficient LoRA and QLoRA fine-tuning using modern tools like Unsloth and Axolotl to create models with specialized skills.
Optimize and Deploy: Convert, quantize, and merge models into the high-performance GGUF format using llama.cpp workflows for deployment in both Ollama and LocalAI.
Build Secure APIs: Architect secure, high-throughput REST APIs for your models using an Nginx reverse proxy for enterprise-grade authentication.
Orchestrate Workflows: Integrate your local models into complex LangChain pipelines to build powerful applications like Retrieval-Augmented Generation (RAG).
Troubleshoot Like a Pro: Diagnose and solve common pitfalls in VRAM management, CUDA conflicts, and performance bottlenecks.
Stop renting your AI. Build, deploy, and own your high-performance LLM infrastructure today.

This item is Non-Returnable

Details

  • ISBN-13: 9798273638556
  • ISBN-10: 9798273638556
  • Publisher: Independently Published
  • Publish Date: November 2025
  • Dimensions: 10 x 7 x 0.26 inches
  • Shipping Weight: 0.5 pounds
  • Page Count: 122

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