Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Mastering LLM Engineering: A Practical Guide to Building and Optimizing Local AI Models for Smart Apps

Mastering LLM Engineering: A Practical Guide to Building and Optimizing Local AI Models for Smart Apps

Carpenter, Alicia B.

Paperback

Series: The Ultimate Tech Guide, Book 8

Programming

ISBN13: 9798282636185
Publisher: Independently Published
Published: May 5 2025
Pages: 172
Weight: 0.45
Height: 0.37 Width: 5.50 Depth: 8.50
Language: English
Unlock the Power of Local AI Models for Smart Apps

In the rapidly evolving landscape of AI, large language models (LLMs) have emerged as a game-changing technology. But with this power comes the challenge of securing and optimizing these models for local deployment. Mastering LLM Engineering: A Practical Guide to Building and Optimizing Local AI Models for Smart Apps is your comprehensive roadmap to navigating this complex field-without relying on cloud-based solutions.

Are you a developer or AI enthusiast looking to integrate cutting-edge LLMs into your apps? This book provides the step-by-step guide you need to deploy, fine-tune, and optimize AI models right on the user's device, ensuring faster, more secure applications with local AI data privacy at the forefront.

By the end of this book, you'll have the expertise to:

  • Understand the core technologies behind LLMs, including transformers, attention mechanisms, and the fine-tuning processes that allow you to adapt these models for specific tasks.
  • Set up a local development environment to deploy LLMs, ensuring secure AI deployment and GDPR-compliant AI models that meet privacy standards.
  • Learn to build and fine-tune models for a variety of applications, from conversational agents to smart assistants, with AI app data security and privacy in AI applications as a central concern.
  • Optimize LLMs for performance-learning techniques for encrypting AI data and minimizing latency while ensuring maximum efficiency in real-time apps.
  • Integrate LLMs into your smart applications, focusing on scalability, security, and user-friendly design, while maintaining control over sensitive data.
  • Troubleshoot and debug LLM-based systems, tackling performance bottlenecks, output errors, and edge cases.
  • Scale your local LLM apps to meet growing demand, adapting to increased workloads and optimizing for cross-platform compatibility, all while ensuring data privacy in LLMs.

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