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Get the lowdown on CockroachDB, the distributed SQL database built to handle the demands of today's data-driven cloud applications. In the second edition of this popular hands-on guide, software developers, architects, and DevOps/SRE teams will learn how to use CockroachDB to create applications that scale elastically and provide seamless delivery for end users while remaining indestructible. Data engineers and database administrators will also learn how to migrate existing applications to CockroachDB's performant, cloud native data architecture. If you're familiar with distributed systems, you'll quickly discover the benefits of strong data correctness and consistency guarantees, as well as optimizations for delivering ultra-low latencies to globally distributed end users. You'll learn how to: Examine the power of and need for distributed SQL and distributed architecture, and their impact on unparalleled resilience Successfully start and manage a project in CockroachDB Design and build applications for distributed infrastructure, including data modeling and schema design Migrate data into CockroachDB Read and write data and run ACID transactions across distributed infrastructure Plan a CockroachDB deployment for resiliency across and multiregion clusters Secure, monitor, and optimize your CockroachDB deployment

With this book, you'll learn how to integrate OpenAI ChatGPT and Whisper into your MLOps pipeline to create conversational AI applications. This integration will enable you to build powerful chatbots, virtual assistants, and other conversational AI applications.

Skill Seeker is a practical solution for tracking growth and leveling up your skills. What if we could gamify these parts of life and gain experience points for learning or doing something new? Skill Seeker does just that in a choose-your-own-adventure-style goal-setting guide book.

This will be an update to the first book, focusing more on physical computing than on craft, designing and implementing new interfaces that are intended for the human form. This book explains how sensors, microcontrollers, and actuators can be incorporated into clothing to create wearable interactive systems.

This insightful guide takes you through the integration of Tableau Pulse and Einstein Copilot, explaining their roles within the broader Tableau and Salesforce ecosystems. Author Ann Jackson, an esteemed analytics professional, offers a step-by-step exploration of these tools.

Until recently, infrastructure was the backbone of organizations operating software they developed in-house. But now that cloud vendors run the computers, companies can finally bring the benefits of agile custom-centricity to their own developers. Adding product management to infrastructure organizations is now all the rage. But how's that possible when infrastructure is still the operational layer of the company? This practical book guides engineers, managers, product managers, and leaders through the shifts that modern platform-led organizations require. You'll learn what platform engineering is--and isn't--and what benefits and value it brings to developers and teams. You'll understand what it means to approach a platform as a product and learn some of the most common technical and managerial barriers to success. With this book, you'll: Cultivate a platform-as-product, developer-centric mindset Learn what platform engineering teams are and are not Start the process of adopting platform engineering within your organization Discover what it takes to become a product manager for a platform team Understand the challenges that emerge when you scale platforms Automate processes and self-service infrastructure to speed development and improve developer experience Build out, hire, manage, and advocate for a platform team

Data engineers proficient in Databricks are currently in high demand. As organizations gather more data than ever before, skilled data engineers on platforms like Databricks become critical to business success. The Databricks Data Engineer Associate certification is proof that you have a complete understanding of the Databricks platform and its capabilities, as well as the essential skills to effectively execute various data engineering tasks on the platform. In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits. You'll also learn to develop ETL pipelines in both batch and streaming modes. Moreover, you'll discover how to orchestrate data workflows and design dashboards while maintaining data governance. Finally, you'll dive into the finer points of exactly what's on the exam and learn to prepare for it with mock tests. Author Derar Alhussein teaches you not only the fundamental concepts but also provides hands-on exercises to reinforce your understanding. From setting up your Databricks workspace to deploying production pipelines, each chapter is carefully crafted to equip you with the skills needed to master the Databricks Platform. By the end of this book, you'll know everything you need to ace the Databricks Data Engineer Associate certification exam with flying colors, and start your career as a certified data engineer from Databricks! You'll learn how to: Use the Databricks Platform and Delta Lake effectively Perform advanced ETL tasks using Apache Spark SQL Design multi-hop architecture to process data incrementally Build production pipelines using Delta Live Tables and Databricks Jobs Implement data governance using Databricks SQL and Unity Catalog Derar Alhussein is a senior data engineer with a master's degree in data mining. He has over a decade of hands-on experience in software and data projects, including large-scale projects on Databricks. He currently holds eight certifications from Databricks, showcasing his proficiency in the field. Derar is also an experienced instructor, with a proven track record of success in training thousands of data engineers, helping them to develop their skills and obtain professional certifications.

Do you want to build web pages but have no prior experience? This friendly guide is the perfect place to start. Youll begin at square one, learning how the web and web pages work, and then steadily build from there. By the end of the book, youll have the skills to create a simple site with multicolumn pages that adapt for mobile devices.Each chapter provides exercises to help you learn various techniques and short quizzes to make sure you understand key concepts.This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels. It is simple and clear enough for beginners, yet thorough enough to be a useful reference for experienced developers keeping their skills up to date.Build HTML pages with text, links, images, tables, and formsUse style sheets (CSS) for colors, backgrounds, formatting text, page layout, and even simple animation effectsLearn how JavaScript works and why the language is so important in web designCreate and optimize web images so theyll download as quickly as possibleNEW! Use CSS Flexbox and Grid for sophisticated and flexible page layoutNEW! Learn the ins and outs of Responsive Web Design to make web pages look great on all devicesNEW! Become familiar with the command line, Git, and other tools in the modern web developers toolkitNEW! Get to know the super-powers of SVG graphics

Learn to leverage Python for advanced 3D data processing, visualization, and AI-enabled workflows. Define 3D virtual realms, immersive environments, and digital automation to analyze physical objects. With a focus on practicality, this book offers data scientists, engineers, and developers a hands-on approach to working with 3D data. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D meshes, images, and more. Author Florent Poux helps you leverage the potential of cutting-edge algorithms and 3D AI models to develop production-ready systems with a focus on automation. You'll learn how to define 3D systems, leverage powerful algorithms, and deploy 3D digital experiences for your industry (geospatial applications, self-driving cars, medical imaging, robotics, manufacturing, computer vision/graphics, and more). This book dives deep into 3D data science with Python: building 3D workflows and wielding Python to implement your solutions. You'll learn: Core concepts and representations of 3D data (meshes, point clouds, voxels, CAD) How to load, manipulate, analyze, and visualize 3D data using powerful Python libraries Advanced AI algorithms for 3D pattern recognition (supervised and unsupervised) 3D reconstruction techniques to generate 3D datasets Automated 3D modeling and generative AI workflows Practical applications of 3D data science in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving How to build end-to-end 3D Python apps through real-world projects Florent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. Heâ s also head professor at the 3D Geodata Academy and innovation director for French Tech 120.

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).

The Make: Math Teacher's Supplement is a guide for teachers, parents and others who are exploring teaching with the authors' Make: Geometry, Make: Trigonometry, or Make: Calculus books. It covers the philosophy behind the books as well as practical tips for managing student 3D printed workflow, classroom technology needed, assessing student understanding, and similar topics. The authors include a list of learning objectives by chapter for all three books, and a matrix of topics covered to simplify adding these materials to existing lesson plans. This guide draws on the the authors' experience training teachers to use 3D printers and OpenSCAD (the math modeling software used in the other books) to summarize what a teacher needs to know before class starts, and tips on learning enough to stay ahead of the students as they explore the 3D printable and other models in the book series. Note that this supplement presumes that the reader has one or more of the author's Make: mathematics books. It is not a "Teacher's Edition" which repeats the content of the regular edition books. Those must be purchased separately.

Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs. Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications. Understand LLM architecture and learn how to best interact with it Design a complete prompt-crafting strategy for an application Gather, triage, and present context elements to make an efficient prompt Master specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG

Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries such as transformers and diffusers, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model or fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train a model that can write text based on your own unique style

Already popular among programmers for its memory safety and speed, the Rust programming language is also valuable for asynchrony. This practical book shows you how asynchronous Rust can help you solve problems that require multitasking. You'll learn how to apply async programming to solve problems with an async approach. You will also dive deeper into async runtimes, implementing your own ways in which async runtimes handle incoming tasks. Authors Maxwell Flitton and Caroline Morton also show you how to implement the Tokio software library to help you with incoming traffic, communicate between threads with shared memory and channels, and design a range of complex solutions using actors. You'll also learn to perform unit and end-to-end tests on a Rust async system. With this book, you'll learn: How Rust approaches async programming How coroutines relate to async Rust Reactive programming and how to implement pub sub in async rust How to solve problems using actors How to customize Tokio to gain control over how tasks are processed Async Rust design patterns How to build an async TCP server just using the standard library How to unit test async Rust By the end of the book, you'll be able to implement your own async TCP server completely from the standard library with zero external dependencies, and unit test your async code.

Learn the 5% of Python programming knowledge which exponentially accelerates your learning curve for the remaining 95%. Alleviate the overwhelm of "too much to learn, not enough time". This book cuts through the noise to focus on the "accelerators" that rapidly move the needle. Discover Python's key abstractions which enable and power the most important Python libraries, including Pandas; Django; Flask; SQLAlchemy; Twisted; Pytest; and more. Top Python programming performance is closer than you think. The difference between the best and the rest lies in the distinctions they make, the mental models they leverage, and their ability to perceive what others cannot. Powerful Python cuts through the noise to focus on these performance accelerators which rapidly improve your coding level and yield the most benefit on real-world production engineering and data teams. After a brief tour of the most necessary programming fundamentals; coding techniques and libraries including Pandas, Django, Flask, SQLAlchemy, Twisted, and Pytest are explored to unlock huge capabilities for the reader. Complex patterns are explained with only the minimum detail needed for use, and frequent code examples are used to show all methods in practice. For those ready to move beyond junior programmer stage, this book provides the 5% of knowledge that makes the remaining 95% of the journey a walk in the park.

This book is a guide to DevOps and software delivery: that is, a guide to the numerous tools and techniques that are required to take that application code and run it and maintain it in production, where it can generate value for your users and your company on an ongoing basis. This includes going through all the modern practices for deploying applications and microservices to the cloud, managing your infrastructure as code, automating your software delivery lifecycle in a CI/CD pipeline, configuring networking, setting up data stores, and hooking up monitoring.

DuckDB is an open source in-process database created for OLAP workloads. It provides key advantages that separate this database from more mainstream OLAP solutions, including embeddability, compatibility with SQL, optimization for fast and efficient analytics, and integration with Python. This practical book shows you how DuckDB leverages Python libraries and tools for data analytics, machine learning, and AI. Author Wei-Meng Lee shows developers, data engineers, data analysts, and data scientists how to get started. You'll learn the primary features and functions of DuckDB, explore use cases and best practices, and examine practical examples of how DuckDB can be used for a variety of data analytics tasks. You'll also dive into specific topics including how to import data into DuckDB, work with tables, perform exploratory data analysis, visualize DuckDB data, perform spatial analysis, and use DuckDB with JSON files, Polars, and JupySQL. You'll also explore: The purpose of DuckDB and its main functions How to conduct data analytics tasks using DuckDB Methods for integrating DuckDB with pandas, Polars, and JupySQL How to use DuckDB to query your data Ways to perform spatial analytics using DuckDB's spatial extension How to work with a diverse range of data including Parquet, CSV, and JSON Wei-Meng Lee is a technologist and founder of Developer Learning Solutions, a company that provides hands-on training on the latest technologies.

Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient.Author Brendan BurnsDirector of Engineering at Microsoft Azuredemonstrates 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.Understand how patterns and reusable components enable the rapid development of reliable distributed systemsUse the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machineExplore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the componentsLearn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows

Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models. Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks. You'll learn: Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful products How to develop an intuition about the Transformer architecture and the impact of each architectural decision Ways to adapt pretrained language models to your own domain and use cases How to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrum Effective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniques How to interface language models with external tools and integrate them into an existing software ecosystem

Take your web development skills from browser to server with Node.js, the popular backend framework used by more than 10 million developers at companies like Amazon, Netflix, and LinkedIn, to name just a few. If you're comfortable working with JavaScript, this practical guide from Samer Buna will show you how to effectively build and maintain even the most complex Node.js applications. Following a hands-on, project-based approach, you'll move from key fundamentals to advanced concepts such as modules, packages, event-driven architecture, streams, child processes, scaling, testing, deployment, and much more--all while focusing on what actually matters in practice. Explore Node.js modules and packages Understand Node.js's event-driven architecture, streams, and child processes Create, test, and maintain efficient and scalable Node.js applications

Business decisions in any context--operational, tactical, or strategic--can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty. With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action--what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future. While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you: The distinction among descriptive, predictive, and prescriptive analytics How predictive analytics produces a menu of action options How prescriptive analytics narrows the menu of action options The forms of prescriptive analytics: eight prescriptive methods Two broad classes of these methods: non-stochastic and stochastic How to develop prescriptive analyses for action recommendations Ways to use an appropriate tool-set in Python

Make: The Complete Guide to Tinkercad embraces the concept of "learn by doing," using 17 fun projects to transform the reader from a novice into a creator of video assets and objects ready for 3D printing. This comprehensive manual teaches the intricacies of the Tinkercad interface, how to model sophisticated objects, and use AI as a tool to solve design challenges. Projects include designing useful and amusing objects such as jewelry, toys, and practical household items. This is not a mere collection of generic online tutorials, but a comprehensive learning experience that will empower the reader to turn their ideas into a physical reality.

What will you learn from this book?This brain-friendly guide teaches you everything from JavaScript language fundamentals to advanced topics, including objects, functions, and the browsers document object model. You wont just be readingyoull be playing games, solving puzzles, pondering mysteries, and interacting with JavaScript in ways you never imagined. And youll write real code, lots of it, so you can start building your own web applications. Prepare to open your mind as you learn (and nail) key topics including:The inner details of JavaScriptHow JavaScript works with the browserThe secrets of JavaScript typesUsing arraysThe power of functionsHow to work with objectsMaking use of prototypesUnderstanding closuresWriting and testing applicationsWhats so special about this book?We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First JavaScript Programming uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep. This book replaces Head First JavaScript, which is now out of print.

Deriving business value from analytics is a challenging process. Turning data into information requires a business analyst who is adept at multiple technologies including databases, programming tools, and commercial analytics tools. This practical guide shows programmers who understand analysis concepts how to build the skills necessary to achieve business value. Author Deanne Larson, data science practitioner and academic, helps you bridge the technical and business worlds to meet these requirements. You'll focus on developing these skills with R and Python using real-world examples. You'll also learn how to leverage methodologies for successful delivery. Learning methodology combined with open source tools is key to delivering successful business analytics and value. This book shows you how to: Apply business analytics methodologies to achieve successful results Cleanse and transform data using R and Python Use R and Python to complete exploratory data analysis Create predictive models to solve business problems in R and Python Use Python, R, and business analytics tools to handle large volumes of data Commit code to GitHub to collaborate with data engineers and data scientists Measure success in business analytics

Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data with the Data Cloud. Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's no-code / low-code functionality to expertly build a Data Cloud foundation that allows you to utilize AI to its fullest, not only within the Salesforce platform but also with third-party machine learning services like AWS SageMaker and Google Vertex. This book will help you: Develop a plan to execute a CDP project effectively and efficiently Connect Data Cloud to external data sources and build out a Customer 360 Data Model Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure Use Salesforce Data Cloud capabilities for identity resolution and segmentation Create calculated, streaming, visualization, and predictive insights Use Data Graphs to power Salesforce Einstein capabilities Learn Data Cloud best practices for all phases of the development lifecycle

Cloud services and SaaS software permeate every company's IT landscape, requiring a shift from manually provisioned services to a more structured approach, with codification at its core. Terraform provides tools to manage the lifecycle of your IT landscape across thousands of different cloud providers and SaaS platforms. By defining your infrastructure as code you can safely and predictably make changes, modularize crucial building blocks, and create reusable service components. Each recipe in this cookbook addresses a specific problem and prefaces the solution with detailed insights into the "how" and "why". If you're just starting with Terraform and codified infrastructure, this book will help you create a solid foundation, on which you can build for years to come. If you're an advanced user, this guide will help you reaffirm your knowledge and take it to the next level, as you challenge yourself with more complex infrastructure, spread across multiple providers. Recipes include: Strategies on how to use Terraform with Version Control Systems Validation and testing patterns for Terraform-managed infrastructure Methods for importing pre-existing resources Transforming infrastructure services into reusable components Integrating Terraform with other HashiCorp tools Deploying Containerized Workloads

This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse

> > > The basic rules for classic charts that are used in 90% of business reports Exceptions to general rules based on real business cases Best practices for dashboard design How to properly set up interactions How to prepare data for advanced visuals How to avoid pitfalls with eye-catching charts