Bøker utgitt av O'Reilly Media

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.

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

> Inside Cyber Warfare features an exclusive deep dive into the wartime operations of an offensive cyber unit of Ukraine's Ministry of Defense as it works to defend the nation against Russian forces, particularly since the 2022 invasion: See what happened when a Ukrainian cyber and special operations team worked together to destroy a secret missile laboratory Explore the legal status of cyber warfare and civilian hackers Discover how a cyber team with little money and limited resources learned to create fire from the manipulation of code in automated systems Distinguish reality from fiction regarding AI safety and existential risk Learn new strategies for keeping you and your loved ones safe in an increasingly complex and insecure world

Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical view of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer who specializes in finance not only has specific data engineering knowledge, but also a good understanding of financial domain-specific problems, approaches, data ecosystem, data providers, data formats, technological constraints, identifiers, entities, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering with real use cases, market practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering Structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source and cloud technologies About the author: Tamer Khraisha is a senior software and data engineer and scientific author with over a decade of experience in the financial sector and academia.

Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets. Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition. With this definitive guide at your side, you'll be able to: Process larger-than-memory datasets at record speed Apply the eager, lazy, and streaming APIs of Polars and decide when to use them Transition smoothly from pandas or Spark to Polars Integrate Polars into your existing code base Work with Arrow and Parquet to efficiently read and write data Translate complex ETL tasks into efficient and elegant queries

With the increasing complexity of modern cloud-based systems, an effective enterprise architecture program is more critical than ever. In this practical book, author Tanu McCabe from Capital One provides proven frameworks and practices to define an effective enterprise architecture strategy--one that will enable software and enterprise architects to create and implement great architecture strategies. You'll learn how to create shared alignment across business and technology, embed architecture practices into processes and tooling, be proactive and reactive to technology and business trends, and instill contextual understanding over siloed decision-making. Complete with examples of patterns and antipatterns, this book provides reusable templates, assessment tools, and practical advice. With this book, you will: Understand exactly what enterprise architecture is, and why it's important to build an effective enterprise architecture practice Learn who needs to be involved to define and implement architecture strategies Examine common pitfalls that inhibit effective architecture strategies Assess the current state of your organization's architecture practice to identify opportunities for improvement Define your own architecture strategy at both an organizational and personal level by applying the book's frameworks Enhance your ability to make great architecture decisions using the book's frameworks and lessons learned Tanusree (Tanu) McCabe is an Executive Distinguished Engineer who leads enterprise architecture strategy at Capital One.

Discover how Delta Lake simplifies the process of building data lakehouses and data pipelines at scale. With this practical guide, data engineers, data scientists, and data analysts will explore key data reliability challenges and learn to apply modern data engineering and management techniques. You'll also understand how ACID transactions bring reliability to data lakehouses at scale. Authors Denny Lee, Prashanth Babu, Tristen Wentling, and Scott Haines explain how to harness the power of Delta Lake to increase your data productivity at scale. You'll learn how to run batch and streaming jobs concurrently on your data lake and accelerate the usability of your data by building effective and high-quality end-to-end pipelines, from data ingestion to analytics. This book helps you: Understand key data reliability challenges Examine data management and engineering techniques using the modern data stack Realize data reliability improvements using Delta Lake Concurrently run streaming and batch jobs against your data lake Execute update, delete, and merge commands Use time travel to rollback and examine previous versions of your data Build a streaming data quality pipeline following the medallion construct About the authors: Denny Lee is a Delta Lake maintainer and Apache Spark and MLflow contributor. Prashanth Babu is a Delta practitioner who works at Databricks. Tristen Wentling is a Delta practitioner who works at Databricks. Scott Haines is an Apache Spark and Delta Lake contributor who works at Nike.

If you're a developer looking to build a distributed, resilient, scalable, high-performance application, you may be evaluating distributed SQL and NoSQL solutions. Perhaps you're considering the Aerospike database. This practical book shows developers, architects, and engineers how to get the highly scalable and extremely low-latency Aerospike database up and running. You will learn how to power your globally distributed applications and take advantage of Aerospike's hybrid memory architecture with the real-time performance of in-memory plus dependable persistence. After reading this book, you'll be able to build applications that can process up to tens of millions of transactions per second for millions of concurrent users on any scale of data. This practical guide provides: Step-by-step instructions on installing and connecting to Aerospike A clear explanation of the programming models available All the advice you need to develop your Aerospike application Coverage of issues such as administration, connectors, consistency, and security Code examples and tutorials to get you up and running quickly And more

The software architect role is evolving. As systems and distributed teams become more complex, it's often impossible for architects to be everywhere they need to be. To be effective, consultants and in-house architects alike have to move constantly from client to client or team to team to collaborate and work with code. And the situation is reaching a breaking point. There's a better way. Andrew Harmel-Law, tech principal at Thoughtworks, shows you how architects and development teams can collaborate effectively and efficiently on the architectures of their systems. Techniques in this book help you ensure that everyone and everything is working toward the same goal. You'll learn how to create a collaborative, decentralized mindset that allows everyone to do architecture and build the best systems they've ever experienced. With this book, you will: Understand the new dynamics that affect modern software delivery and how to take advantage of them to optimize for fast flow and continuous feedback Learn a methodology that brings software architecture and development together in partnership Nurture the fundamental interplay of decisions, advice, autonomy, and architecture Initiate practices and constraints that maximize benefits and mitigate risks Create an approach tuned to your skill sets, architecture, and your organization's engineering culture Identify and work to prevent failure modes when they threaten to arise

To ensure that applications are reliable and always available, more businesses today are moving applications to AWS. But many companies still struggle to design and build these cloud applications effectively, thinking that because the cloud is resilient, their applications will be too. With this practical guide, software, DevOps, and cloud engineers will learn how to implement resilient designs and configurations in the cloud using hands-on independent labs. Authors Kevin Schwarz, Jennifer Moran, and Dr. Nate Bachmeier from AWS teach you how to build cloud applications that demonstrate resilience with patterns like back off and retry, multi-Region failover, data protection, and circuit breaker with common configuration, tooling, and deployment scenarios. Labs are organized into categories based on complexity and topic, making it easy for you to focus on the most relevant parts of your business. You'll learn how to: Configure and deploy AWS services using resilience patterns Implement stateless microservices for high availability Consider multi-Region designs to meet business requirements Implement backup and restore, pilot light, warm standby, and active-active strategies Build applications that withstand AWS Region and Availability Zone impairments Use chaos engineering experiments for fault injection to test for resilience Assess the trade-offs when building resilient systems, including cost, complexity, and operational burden

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. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list--a feat accomplished by more than 400 industry experts--this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization

Learn how to use Go's strengths to develop services that are scalable and resilient even in an unpredictable environment. With this book's expanded second edition, Go developers will explore the composition and construction of cloud native applications, from lower-level Go features and mid-level patterns to high-level architectural considerations. Each chapter in this new edition builds on the lessons of the previous chapter, taking intermediate to advanced developers through Go to construct a simple but fully featured distributed key-value store. You'll learn about Go generics, dependability and reliability, memory leaks, and message-oriented middleware. New chapters on security and distributed state delve into critical aspects of developing secure distributed cloud native applications. With this book you will: Learn the features that make Go an ideal language for building cloud native software Understand how Go solves the challenges of designing scalable distributed services Design and implement a reliable cloud native service by leveraging Go's lower-level features such as channels and goroutines Apply patterns, abstractions, and tooling to effectively build and manage complex distributed systems Overcome stumbling blocks when using Go to build and manage a cloud native service

Looking to leverage artificial intelligence in your products? This comprehensive book provides a clear and actionable framework to help product managers build AI-powered products that meet the needs of users and drive business growth. Author Marily Nika guides you through the most popular use cases that AI can solve and describes the technologies you can leverage for each use case. With this book, you'll quickly learn the tools and skill set you need throughout the AI product life cycle, and you'll understand how to create effective roadmaps for the products you're building. By the end of the book, AI product managers will be well-versed in AI and machine learning basics and will learn how to make the right trade-offs, identify the right use cases, and work effectively with research and engineering teams. This guide will help you: Build a framework that identifies opportunities for leveraging AI in products Understand the use cases that AI can help you solve Create a team with the skills and expertise to execute AI-powered products Learn best practices for measuring the success of AI-powered products Understand the ethical and legal considerations that come with building AI products Collaborate with scientists, engineers, and key stakeholders

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley and Manish Mathur from Google's Healthcare and Life Sciences Industry team help you explore real-world applications of these technologies in healthcare, from personalized patient care and drug discovery to enhanced medical imaging and robot-assisted surgeries. You'll also learn the challenges of using these technologies--and the ethical implications of their application in this field. With this book, you will: Learn how LLMs and generative AI can help address and transform healthcare issues Explore the basics of LLMs and generative AI and learn how they work Learn how these technologies are being applied in healthcare today Understand several LLM and generative AI use cases Examine the ethics and challenges of applying LLMs and generative AI to healthcare Understand the potential use of LLMs and generative AI in healthcare in the near term and their prospects for the future

Learn how to get started with Futures Thinking. With this practical guide, Phil Balagtas, founder of the Design Futures Initiative and the global Speculative Futures network, shows you how designers and futurists have made futures work at companies such as Atari, IBM, Apple, Disney, Autodesk, Lufthansa, and McKinsey & Company. This book demystifies the process of Futures Thinking into a language that's practical and useful for both designers and strategists. You'll learn about Strategic Foresight for using ideas about the future to anticipate and prepare for change; explore Speculative Design to deal with the relationship between science, technology, and humans; and Design Fiction to explore and critique possible futures. Balagtas also shares stories from his journey to build a global community and describes how he works with clients to reshape the futures vocabulary. With this guide, you'll learn how to: Prepare your client, team, and/or audience for futures Facilitate and work with the fundamental methods and frameworks Gain advocacy and support within your organization Provide measurable value from the process and outcomes Build a futures culture and team Sustain a culture and support system beyond projects

As the transformation to hybrid multicloud accelerates, businesses require a structured approach to securing their workloads. Adopting zero trust principles demands a systematic set of practices to deliver secure solutions. Regulated businesses, in particular, demand rigor in the architectural process to ensure the effectiveness of security controls and continued protection. This book provides the first comprehensive method for hybrid multicloud security, integrating proven architectural techniques to deliver a comprehensive end-to-end security method with compliance, threat modeling, and zero trust practices. This method ensures repeatability and consistency in the development of secure solution architectures. Architects will learn how to effectively identify threats and implement countermeasures through a combination of techniques, work products, and a demonstrative case study to reinforce learning. You'll examine: The importance of developing a solution architecture that integrates security for clear communication Roles that security architects perform and how the techniques relate to nonsecurity subject matter experts How security solution architecture is related to design thinking, enterprise security architecture, and engineering How architects can integrate security into a solution architecture for applications and infrastructure using a consistent end-to-end set of practices How to apply architectural thinking to the development of new security solutions About the authors Mark Buckwell is a cloud security architect at IBM with 30 years of information security experience. Carsten Horst with more than 20 years of experience in Cybersecurity is a certified security architect and Associate Partner at IBM. Stefaan Van daele has 25 years experience in Cybersecurity and is a Level 3 certified security architect at IBM.

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting--especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

"A learner's guide to real-world programming with C# and .NET"--Cover.

Frontend developers have to consider many things: browser compatibility, usability, performance, scalability, SEO, and other best practices. But the most fundamental aspect of creating websites is one that often falls short: accessibility. Accessibility is the cornerstone of any website, and if a website is inaccessible, users won't be able to interact with it, obtain information, sign up for services, or buy products. The Web Accessibility Cookbook provides you with dozens of recipes to help you avoid these failures. You'll learn how to build common components, such as main navigation, filters, and dialogs, in an accessible manner. Each recipe not only explains how to build things but also why. Author Manuel Matuzovic provides the knowledge you need to create your own accessible components and address your users' varying needs, abilities, and preferences. With this practical guide, you will: Learn how to build websites that feature inclusive frontends Discover the common obstacles website users face every day Understand how your decisions impact users Learn how to build accessible frontends step-by-step Write high-quality markup and CSS Evaluate the accessibility of frontend components