Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

• Understand what AI engineering is and how it differs from traditional machine learning engineering
• Learn the process for developing an AI application, the challenges at each step, and approaches to address them
• Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work
• Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them
• Choose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse 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.

• Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
• Make informed decisions by identifying the strengths and weaknesses of different tools
• Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
• Understand the distributed systems research upon which modern databases are built
• Peek behind the scenes of major online services, and learn from their architectures

Updated for both Python 3.4 and 2.7, this convenient pocket guide is the perfect on-the-job quick reference. Youâ ll find concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools. The handy index lets you pinpoint exactly what you need.

Written by Mark Lutzâ widely recognized as the worldâ s leading Python trainerâ Python Pocket Reference is an ideal companion to Oâ Reillyâ s classic Python tutorials, Learning Python and Programming Python, also written by Mark.

This fifth edition covers:

• Built-in object types, including numbers, lists, dictionaries, and more
• Statements and syntax for creating and processing objects
• Functions and modules for structuring and reusing code
• Pythonâ s object-oriented programming tools
• Built-in functions, exceptions, and attributes
• Special operator overloading methods
• Widely used standard library modules and extensions
• Command-line options and development tools
• Python idioms and hints
• The Python SQL Database API

AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today.

You'll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings.

This book also helps you:

• Understand the architecture of Transformer language models that excel at text generation and representation
• Build advanced LLM pipelines to cluster text documents and explore the topics they cover
• Build semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankers
• Explore how generative models can be used, from prompt engineering all the way to retrieval-augmented generation
• Gain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

• Use Scikit-learn to track an example ML project end to end
• Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
• Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
• Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
• Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.

You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way.

You'll understand how to:

• Visualize: Create plots for data exploration and communication of results
• Transform: Discover variable types and the tools to work with them
• Import: Get data into R and in a form convenient for analysis
• Program: Learn R tools for solving data problems with greater clarity and ease
• Communicate: Integrate prose, code, and results with Quarto

Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time--beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills.

With this popular hands-on guide at your side, you'll get:

• A grounding in the syntax and semantics of the Python language
• A clear definition of each programming concept, with emphasis on clear vocabulary
• How to work with variables, statements, functions, and data structures in a logical progression
• Techniques for reading and writing files and databases
• A solid understanding of objects, methods, and object-oriented programming
• Debugging strategies for syntax, runtime, and semantic errors
• An introduction to recursion, interface design, data structures, and basic algorithms
• How to use LLMs--including effective prompts, testing code, and debugging
• And more

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.

Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology.

This book will help you:

• Get a concise overview of the entire data engineering landscape
• Assess data engineering problems using an end-to-end framework of best practices
• Cut through marketing hype when choosing data technologies, architecture, and processes
• Use the data engineering lifecycle to design and build a robust architecture
• Incorporate data governance and security across the data engineering lifecycle

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. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.

Learn how to empower AI to work for you. This book explains:

• The structure of the interaction chain of your program's AI model and the fine-grained steps in between
• How AI model requests arise from transforming the application problem into a document completion problem in the model training domain
• The influence of LLM and diffusion model architecture--and how to best interact with it
• How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

• Use the Jupyter notebook and IPython shell for exploratory computing
• Learn basic and advanced features in NumPy
• Get started with data analysis tools in the pandas library
• Use flexible tools to load, clean, transform, merge, and reshape data
• Create informative visualizations with matplotlib
• Apply the pandas groupby facility to slice, dice, and summarize datasets
• Analyze and manipulate regular and irregular time series data
• Learn how to solve real-world data analysis problems with thorough, detailed examples

For years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesn't serve the industry well--or the engineer. The staff engineer's path allows engineers to contribute at a high level as role models, driving big projects, determining technical strategy, and raising everyone's skills.

This in-depth book shows you how to understand your role, manage your time, master strategic thinking, and set the standard for technical work. You'll read about how to be a leader without direct authority, how to plan ahead to make the right technical decisions, and how to make everyone around you better, while still growing as an expert in your domain.

By exploring the three pillars of a staff engineer's job, Tanya Reilly, a veteran of the staff engineer track, shows you how to:

• Take a broad, strategic view when thinking about your work
• Dive into practical tactics for making projects succeed
• Determine what good engineering means in your organization

Build interactive, data-driven websites with the potent combination of open source technologies and web standards, even if you have only basic HTML knowledge. With the latest edition of this popular hands-on guide, you'll tackle dynamic web programming using the most recent versions of today's core technologies: PHP, MySQL, JavaScript, CSS, HTML5, jQuery, Node.js, and the powerful React library.

Web designers will learn how to use these technologies together while picking up valuable web programming practices along the way, including how to optimize websites for mobile devices. You'll put everything together to build a fully functional social networking site suitable for both desktop and mobile browsers.

• Explore MySQL from database structure to complex queries
• Use the MySQL PDO extension, PHP's improved MySQL interface
• Create dynamic PHP web pages that tailor themselves to the user
• Manage cookies and sessions and maintain a high level of security
• Enhance JavaScript with the React library
• Use Ajax calls for background browser-server communication
• Style your web pages by acquiring CSS skills
• Implement HTML5 features including geolocation, audio, video, and the canvas element
• Reformat your websites into mobile web apps

Managing people is difficult wherever you work. But in the tech industry, where management is also a technical discipline, the learning curve can be brutal--especially when there are few tools, texts, and frameworks to help you. In this practical guide, author Camille Fournier (tech lead turned CTO) takes you through each stage in the journey from engineer to technical manager.

From mentoring interns to working with senior staff, you'll get actionable advice for approaching various obstacles in your path. This book is ideal whether you're a new manager, a mentor, or a more experienced leader looking for fresh advice. Pick up this book and learn how to become a better manager and leader in your organization.

• Begin by exploring what you expect from a manager
• Understand what it takes to be a good mentor, and a good tech lead
• Learn how to manage individual members while remaining focused on the entire team
• Understand how to manage yourself and avoid common pitfalls that challenge many leaders
• Manage multiple teams and learn how to manage managers
• Learn how to build and bootstrap a unifying culture in teams

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, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.

This book will help you tackle scenarios such as:

• Engineering data and choosing the right metrics to solve a business problem
• Automating the process for continually developing, evaluating, deploying, and updating models
• Developing a monitoring system to quickly detect and address issues your models might encounter in production
• Architecting an ML platform that serves across use cases
• Developing responsible ML systems

Don't waste time bending Python to fit patterns you've learned in other languages. Python's simplicity lets you become productive quickly, but often this means you aren't using everything the language has to offer. With the updated edition of this hands-on guide, you'll learn how to write effective, modern Python 3 code by leveraging its best ideas.

Discover and apply idiomatic Python 3 features beyond your past experience. Author Luciano Ramalho guides you through Python's core language features and libraries and teaches you how to make your code shorter, faster, and more readable.

Complete with major updates throughout, this new edition features five parts that work as five short books within the book:

• Data structures: Sequences, dicts, sets, Unicode, and data classes
• Functions as objects: First-class functions, related design patterns, and type hints in function declarations
• Object-oriented idioms: Composition, inheritance, mixins, interfaces, operator overloading, protocols, and more static types
• Control flow: Context managers, generators, coroutines, async/await, and thread/process pools
• Metaprogramming: Properties, attribute descriptors, class decorators, and new class metaprogramming hooks that replace or simplify metaclasses

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. Whether you're already leading product teams or are an aspiring product manager, and regardless of your prior knowledge with AI, this guide will empower you to confidently navigate every stage of the AI product lifecycle.

• Confidently manage AI product development with tools, frameworks, strategic insights, and real-world examples from Google, Meta, OpenAI, and more
• Lead product orgs to solve real problems via agentic AI and GenAI capabilities
• Gain AI Awareness and technical fluency to work with AI models, LLMs, and the algorithms that power them; get cross-functional alignment; make strategic trade-offs; and set OKRs

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how:

• IPython and Jupyter provide computational environments for scientists using Python
• NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
• Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
• Matplotlib includes capabilities for a flexible range of data visualizations
• Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction.

This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without.

• Understand Lean Startup, analytics fundamentals, and the data-driven mindset
• Look at six sample business models and how they map to new ventures of all sizes
• Find the One Metric That Matters to you
• Learn how to draw a line in the sand, so you'll know it's time to move forward
• Apply Lean Analytics principles to large enterprises and established products

An understanding of psychology-specifically the psychology behind how users behave and interact with digital interfaces-is perhaps the single most valuable nondesign skill a designer can have. The most elegant design can fail if it forces users to conform to the design instead of working within the blueprint of how humans perceive and process the world around them.

This practical guide explains how you can apply key principles of psychology to build products and experiences that are more human-centered and intuitive. Author Jon Yablonski deconstructs familiar apps and experiences to provide clear examples of how UX designers can build interfaces that adapt to how users perceive and process digital interfaces.

You'll learn:

• How aesthetically pleasing design creates positive responses
• The principles of psychology most useful for designers
• How these psychology principles relate to UX heuristics
• Predictive models including Fitts's law, Jakob's law, and Hick's law
• Ethical implications of using psychology in design
• A practical framework for applying principles of psychology in your design process

This updated edition includes an even deeper connection to the underlying psychological concepts that govern the principles explored in the book, along with accompanying UX methods and techniques. Examples have been updated to ensure the deconstructed apps and experiences remain familiar and relevant.

What will you learn from this book?

The new edition of this brain-friendly guide takes you through a comprehensive journey into modern JavaScript, covering everything from core language fundamentals to today's cutting-edge features. You'll dive into the nuances of JavaScript types and the unparalleled flexibility of its functions. You'll also learn how to expertly navigate classes and objects, and finally understand closures. But that's just the beginning. You'll also get hands-on with the browser's document object model (DOM), engaging with JavaScript in exciting ways. You won't just be reading--you'll be playing games, solving puzzles, pondering mysteries, and interacting with JavaScript as never before. And you'll write real code, lots of it, so you can start building your own applications.

What's so special about this book?

If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. With this book, you'll learn JavaScript through a multisensory experience that engages your mind--rather than a text-heavy approach that puts you to sleep.