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Building AI Intensive Python Applications
Building AI Intensive Python Applications

Building AI Intensive Python Applications: Create intelligent apps with LLMs and vector databases

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Profile Icon Rachelle Palmer Profile Icon Ben Perlmutter Profile Icon Ashwin Gangadhar Profile Icon Nicholas Larew Profile Icon Sigfrido Narváez Profile Icon Thomas Rueckstiess Profile Icon Henry Weller Profile Icon Richmond Alake Profile Icon Shubham Ranjan +5 more Show less
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₹799.99 ₹4468.99
eBook Sep 2024 298 pages 1st Edition
eBook
₹799.99 ₹4468.99
Paperback
₹5585.99
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Renews at ₹800p/m
Arrow left icon
Profile Icon Rachelle Palmer Profile Icon Ben Perlmutter Profile Icon Ashwin Gangadhar Profile Icon Nicholas Larew Profile Icon Sigfrido Narváez Profile Icon Thomas Rueckstiess Profile Icon Henry Weller Profile Icon Richmond Alake Profile Icon Shubham Ranjan +5 more Show less
Arrow right icon
₹799.99 ₹4468.99
eBook Sep 2024 298 pages 1st Edition
eBook
₹799.99 ₹4468.99
Paperback
₹5585.99
Subscription
Free Trial
Renews at ₹800p/m
eBook
₹799.99 ₹4468.99
Paperback
₹5585.99
Subscription
Free Trial
Renews at ₹800p/m

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Building AI Intensive Python Applications

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Key benefits

  • Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks
  • Implement effective retrieval-augmented generation strategies with MongoDB Atlas
  • Optimize AI models for performance and accuracy with model compression and deployment optimization
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.

Who is this book for?

This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.

What you will learn

  • Understand the architecture and components of the generative AI stack
  • Explore the role of vector databases in enhancing AI applications
  • Master Python frameworks for AI development
  • Implement Vector Search in AI applications
  • Find out how to effectively evaluate LLM output
  • Overcome common failures and challenges in AI development

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 06, 2024
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207245
Vendor :
MongoDB
Category :
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Product feature icon Download this book in EPUB and PDF formats
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Product Details

Publication date : Sep 06, 2024
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207245
Vendor :
MongoDB
Category :
Languages :
Concepts :
Tools :

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Table of Contents

16 Chapters
Chapter 1: Getting Started with Generative AI Chevron down icon Chevron up icon
Chapter 2: Building Blocks of Intelligent Applications Chevron down icon Chevron up icon
Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design Chevron down icon Chevron up icon
Chapter 3: Large Language Models Chevron down icon Chevron up icon
Chapter 4: Embedding Models Chevron down icon Chevron up icon
Chapter 5: Vector Databases Chevron down icon Chevron up icon
Chapter 6: AI/ML Application Design Chevron down icon Chevron up icon
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search Chevron down icon Chevron up icon
Chapter 7: Useful Frameworks, Libraries, and APIs Chevron down icon Chevron up icon
Chapter 8: Implementing Vector Search in AI Applications Chevron down icon Chevron up icon
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics Chevron down icon Chevron up icon
Chapter 9: LLM Output Evaluation Chevron down icon Chevron up icon
Chapter 10: Refining the Semantic Data Model to Improve Accuracy Chevron down icon Chevron up icon
Chapter 11: Common Failures of Generative AI Chevron down icon Chevron up icon
Chapter 12: Correcting and Optimizing Your Generative AI Application Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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