Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, cofounder of Claypot AI, considers each design decision in the context of how it can help your system as a whole achieve its objectives.

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice.

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

Messy code is a nuisance. "Tidying" code, to make it more readable, requires breaking it up into manageable sections. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings to improve your code while keeping the overall structure of the system in mind. Instead of trying to master tidying all at once, this book lets you try out a few examples that make sense for your problem. If you have a big function containing many lines of code, you'll learn how to logically divide it into smaller chunks.

There are no easy decisions in software architecture. Instead, there are many hard parts—difficult problems or issues with no best practices—that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture.

The difference between growing your business and going out of business is your ability to think strategically. The problem is, most leaders are stuck in the operational weeds, struggling to find the time to make better strategic decisions. This challenge is only heightened by the rise of AI. While you know AI is the future, current demands leave you with no time to explore its benefits. Meanwhile, your competition is gaining an edge by integrating AI. The time to act is now.

Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.

As organizations shift from monolithic applications to smaller, self-contained microservices, distributed systems have become more fine-grained. But developing these new systems brings its own host of problems. This expanded second edition takes a holistic view of topics that you need to consider when building, managing, and scaling microservices architectures. Through clear examples and practical advice, author Sam Newman gives everyone from architects and developers to testers and IT operators a firm grounding in the concepts.

In the late 1850s, a physician in New York City used a syringe and glass tube to inject half a drop of sperm into a woman's uterus, marking the first recorded instance of artificial insemination. From that day forward, doctors and scientists have turned to technology in ever more innovative ways to facilitate conception. Fertility Technology surveys this history in all its medical, practical, and ethical complexity, and offers a look at state-of-the-art fertility technology in various social and political contexts around the world.

Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models.

When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it's often difficult to understand what each one offers. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.

Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works, why it often doesn’t, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil—products that don’t work, and probably never will.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

In this book, author Will Larson shows you ways to obtain your first executive job and quickly ramp up to meet the challenges you may not have encountered in non-executive roles: measuring engineering for both engineers and the CEO, company-scoped headcount planning, communicating successfully across a growing organization, and figuring out what people actually mean when they keep asking for a "technology strategy."

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, cofounder of Claypot AI, considers each design decision in the context of how it can help your system as a whole achieve its objectives.

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice.

In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

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.

This is a practical, non-technical guide for business leaders, entrepreneurs, and curious minds. This comprehensive guide on agentic AI cuts through the hype and offers a clear, jargon-free strategic roadmap to understanding and applying this technology. The authors bring a rare perspective, having implemented agentic AI across diverse organizations—from global enterprises to agile startups—witnessing both remarkable successes and sobering failures.

Ready to build a passive income stream that works while you sleep? This game-changing book reveals how to harness AI-powered tools like ChatGPT to generate effortless earnings—without the grind of traditional work.

From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks.

Harness the full potential of generative AI to improve your productivity, transform your career, and boost your creativity! Brought to you by the Amazon bestselling author of Essentials of AI for Beginners, this practical, engaging guide makes generative AI easy to understand and use—no matter your background, goals, or starting point. This book stands out for its in-depth yet accessible content, so you can learn core concepts without the jargon. It also includes real-world use cases to discover how AI is already transforming diverse industries. This title also offers a balanced perspective.

Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

Drawing from her experience at Google and Meta, Dr. Marily Nika delivers the definitive guide for product managers building AI and GenAI powered products. Packed with smart strategies, actionable tools, and real-world examples, this book breaks down the complex world of AI agents and generative AI products into a playbook for driving innovation to help product leaders bridge the gap between niche AI and GenAI technologies and user pain points.

Financial Freedom and Becoming a Millionaire Using Artificial Intelligence. Why You Can't Afford to Miss This Book Comprehensive and Updated: Authored by Benjamin Stone, this guide provides you with the most current and comprehensive information on ChatGPT, updated for GPT-4. From Novice to Pro: Whether you're a beginner in the world of AI, a nonfiction author, or a seasoned expert, this blueprint is designed to take your skills and income to the next level. Skyrocket Your Productivity: Learn how to harness the power of ChatGPT to complete projects at unprecedented speeds, whether you're ...

Are you fascinated by artificial intelligence but overwhelmed by complex jargon or unsure where to start? You're not alone—and this guide is here to help. Whether you're exploring AI to boost your career, optimize your business, or simply stay ahead of the curve, Launch into AI Mastery offers a beginner-friendly roadmap designed for adults with busy schedules and no technical background.

Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.