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Complete eight data science projects that lock in important real world skills-along with a practical process you can use to learn any new technique quickly and efficiently.

Whether you know Python or not, this book will help you write amazing Python code using the latest versions of Copilot or ChatGPT.Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way—with the help of AI. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming, Second Edition you’ll learn how to: • Write fun and useful Python applications—no programming experience required! • Use the GitHub Copilot AI coding assistant to create Python programs • Write prompts that tell Copilot exactly what to do • Read Python code and understand what it does • Test your programs to make sure they work the way you want them to • Fix code with prompt engineering or human tweaks • Apply Python creatively to help out on the job AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it teaches you everything you need to start programming Python in an AI-first world. You’ll learn skills you can use to create working apps for data analysis, automating tedious tasks, and even video games. Plus, in this new edition, you’ll find groundbreaking techniques for breaking down big software projects into smaller tasks AI can easily achieve. Foreword by Beth Simon. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. About the book This book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates. What's inside • Prompts for working code • Tweak code manually and with AI help • AI-test your programs • Let AI handle tedious details About the reader If you can move files around on your computer and install new programs, you can learn to write useful software! About the author Dr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan. Table of Contents 1 Introducing AI-assisted programming with GitHub Copilot 2 Getting started with Copilot 3 Designing functions 4 Reading Python code: Part 1 5 Reading Python code: Part 2 6 Testing and prompt engineering 7 Problem decomposition 8 Debugging and better understanding your code 9 Automating tedious tasks 10 Making some games 11 Creating an authorship identification program 12 Future directions

Learn how to put Large Language Model-based applications into production safely and efficiently.This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. Table of Contents 1 Words’ awakening: Why large language models have captured attention 2 Large language models: A deep dive into language modeling 3 Large language model operations: Building a platform for LLMs 4 Data engineering for large language models: Setting up for success 5 Training large language models: How to generate the generator 6 Large language model services: A practical guide 7 Prompt engineering: Becoming an LLM whisperer 8 Large language model applications: Building an interactive experience 9 Creating an LLM project: Reimplementing Llama 3 10 Creating a coding copilot project: This would have helped you earlier 11 Deploying an LLM on a Raspberry Pi: How low can you go? 12 Production, an ever-changing landscape: Things are just getting started A History of linguistics B Reinforcement learning with human feedback C Multimodal latent spaces

Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully.

An in-depth guide to everything Terraform, complete with newly established best practices and experienced insights into Infrastructure as Code.Terraform and its open-source fork OpenTofu’s “Infrastructure as Code (IaC)” approach has redefined the way you manage your infrastructure. Its premise is simple-yet-awesome: provision, update, scale, and replicate your infrastructure with the same ease as your application code. In Terraform in Depth, you’ll discover absolutely everything you need to automate and manage your infrastructure with just a few lines of code. Inside Terraform in Depth, you’ll learn how to: • Understand and write basic Terraform code • Avoid vendor lock-in with the open source OpenTofu • Switch between OpenTofu and Terraform as needed • Construct continuous integration and continuous delivery (CI/CD) pipelines for Terraform • Organize Terraform projects and modules for team-based, production use • Develop and test robust Terraform modules • Create custom Terraform providers Terraform in Depth is fully up to date with the latest versions, standards, and approaches of Terraform and OpenTofu. Complete and comprehensive, its one-stop approach covers everything from Terraform and OpenTofu’s absolute basics all the way to advanced production uses. Every technique is illustrated with the kind of real-world examples infrastructure engineers encounter every day. Forewords by Anton Babenko and Christian Mesh. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Terraform and its open-source fork OpenTofu practically eliminate manual infrastructure configuration. With the Terraform infrastructure management tool, even complex operations that used to require kludgy scripts and time-sucking tinkering can be created, managed, and shared as an organized codebase. Master Terraform, and you’ll be able to update a fleet of machines with just a few lines of code. About the book Terraform in Depth teaches Terraform techniques and Infrastructure as Code (IaC) practices that you can use to deploy and manage applications in the cloud or your on-prem data center. Each chapter includes interesting hands-on examples, such as creating a flexible Terraform module and debugging Terraform plans. You’ll quickly learn to define your infrastructure with Terraform. Then, you’ll dive into advanced applications, including CI/CD pipelines, creating tools for documentation and security, and Terraform code management. What's inside • Understand and write basic Terraform code • Avoid vendor lock-in with OpenTofu • Construct CI/CD pipelines • Develop and test Terraform modules About the reader For sysadmins, software developers, and cloud engineers famil- iar with the CLI. About the author Robert Hafner has led engineering efforts at numerous startups, including Malwarebytes, Vicarious AI, and Rad AI. He is currently a Distinguished Engineer at a Fortune 100 Telecom. Table of Contents Part 1 1 A brief overview of Terraform 2 Terraform HCL components 3 Terraform variables and modules 4 Expressions and iterations 5 The Terraform plan Part 2 6 State management 7 Code quality and continuous integration 8 Continuous delivery and deployment 9 Testing and refactoring Part 3 10 Advanced Terraform topics 11 Alternative interfaces 12 Terraform providers

Speed up common data science tasks with AI assistants like ChatGPT and Large Language Models (LLMs) from Anthropic, Cohere, AI21, Hugging Face, and more!Using ChatGPT and other AI-powered tools, you can analyze almost any kind of data with just a few short lines of plain English. In LLMs in Action, you’ll learn important techniques for streamlining your data science practice, expanding your skillset and saving you hours—or even days—of time. Inside, you’ll learn how to use AI assistants to: • Analyze text, tables, images, and audio files • Extract information from multi-modal data lakes • Classify, cluster, transform, and query multimodal data • Build natural language query interfaces over structured data sources • Use LangChain to build complex data analysis pipelines • Prompt engineering and model configuration This practical book takes you from your first prompts through advanced techniques like building automated analysis pipelines and fine-tuning existing models. You’ll learn how to create meaningful reports, generate informative graphs, and much more. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book LLMs in Action teaches you to use a new generation of AI assistants and Large Language Models (LLMs) to simplify and accelerate common data science tasks. Cornell professor and long-time LLM advocate Immanuel Trummer reveals techniques he’s pioneered for getting the most out of LLMs in data science, including model selection and specialization, techniques for tuning parameters, and reliable prompt templates. You’ll start with an in-depth exploration of how LLMs work. Then, you’ll dive into no-code data analysis with LLMs, creating custom operators with the OpenAI Python API, and building complex data analysis pipelines with the cutting edge LangChain framework. About the reader For data scientists, data analysts, and others who are interested in making their work easier through the use of artificial intelligence techniques. Readers should have a basic understanding of the Python programming language. About the author Immanuel Trummer is an assistant professor for computer science at Cornell University and leader of the Cornell Database Group. His papers have been selected for “Best of VLDB”, “Best of SIGMOD”, for the ACM SIGMOD Research Highlight Award, and for publication in CACM as CACM Research Highlight. Immanuel’s online course on data management has reached over a million views on YouTube. Over the past few years, his group has published extensively on projects that apply large language models in the context of data science.

A friendly illustrated guide to designing and implementing your first database.Data is the backbone of computer science, and databases are the main way that data is stored, exchanged, manipulated, and managed. Whether you’re a software developer, a data scientist, or an enthusiastic business user looking to up your data analysis skills, it pays to learn how to create and query relational databases like MySQL, SQL Server, PostgreSQL, and Oracle. Grokking Relational Database Design will get you started! In Grokking Relational Database Design, you’ll learn how to: • Query and create databases using Structured Query Language (SQL) • Design databases from scratch • Implement and optimize database designs • Take advantage of generative AI when designing databases A well-constructed database is easy to understand, query, manage, and scale when your app needs to grow. In Grokking Relational Database Design you’ll learn the basics of relational database design including how to name fields and tables, which data to store where, how to eliminate repetition, good practices for data collection and hygiene, and much more. You won’t need a computer science degree or in-depth knowledge of programming—the book’s practical examples and down-to-earth definitions are beginner-friendly. About the book Grokking Relational Database Design teaches the art of database design through real-world projects, insightful illustrations, and action-oriented learning. Unlike many beginning database books that focus on the technical details of SQL and formal database theory, this book teaches you how to think about relational database design from the ground up, so you’ll create databases that are a joy to use for a long time. Everything in this book is reinforced by hands-on exercises and examples. You’ll quickly design, implement, and optimize a database for an e-commerce application like the ones you use every day. You’ll also explore how generative AI tools such as ChatGPT radically simplify the mundane tasks of database design. About the reader Suitable for self-taught programmers, engineers, data scientists, and business data users. No previous experience with relational databases required. About the author Qiang Hao is an associate professor of Computer Science at Western Washington University. He is a recognized expert in computing education research and has extensive experience in teaching a variety of computer science courses, such as software engineering and database systems. Michail Tsikerdekis is an associate professor of Computer Science at Western Washington University. He holds a Ph.D. in Informatics from Masaryk University, Czechia. Additionally, he is recognized as an IEEE Senior Member, and his expertise covers over a decade of teaching experience in Computer Science and Cybersecurity.

Learn how to handle errors, inefficiencies, and outdated paradigms by exploring the most common mistakes you’ll find in production C++ code.100 C++ Mistakes and How To Avoid Them reveals the problems you’ll inevitably encounter as you write new C++ code and diagnose legacy applications, along with practical techniques you need to resolve them. Inside 100 C++ Mistakes and How To Avoid Them you’ll learn how to: • Design solid classes • Minimize resource allocation/deallocation issues • Use new C++ features • Identify the differences between compile and runtime issues • Recognize C-style idioms that miss C++ functionality • Use exceptions well 100 C++ Mistakes and How To Avoid Them gives you practical insights and techniques to improve your C++ coding kung fu. Author Rich Yonts has been using C++ since its invention in the 1980s. This book distills that experience into practical, reusable advice on how C++ programmers at any skill level can improve their code. Unlike many C++ books that concentrate on language theory and toy exercises, this book is loaded with real examples from production codebases. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Over ten billion lines of C++ code are running in production applications, and 98-developers find and fix mistakes in them every day. Even mission-critical applications have bugs, performance inefficiencies, and readability problems. This book will help you identify them in the code you’re maintaining and avoid them in the code you’re writing. About the book 100 C++ Mistakes and How To Avoid Them presents practical techniques to improve C++ code, from legacy applications to modern codebases that use C++ 11 and beyond. Author Rich Yonts provides a concrete example to illustrate each issue, along with a step-by-step walkthrough for improving readability, effectiveness, and performance. Along the way, you’ll even learn how and where to replace outdated patterns and idioms with modern C++. What's inside • Design solid classes • Resource allocation/deallocation issues • Compile and runtime problems • Replace C-style idioms with proper C++ About the reader Covers C++ 98 through 23, with an emphasis on diagnosing and improving legacy code. About the author Rich Yonts is a Senior Software Engineer at Teradata and a long-time software engineer using C++, Java, and Python. He has held a number of technical and leadership roles during his many years at IBM and Sony. Table of Contents 1 C++: With great power comes great responsibility Part 1 2 Better modern C++: Classes and types 3 Better modern C++: General programming 4 Better modern C++: Additional topics Part 2 5 C idioms 6 Better premodern C++ Part 3 7 Establishing the class invariant 8 Maintaining the class invariant 9 Class operations 10 Exceptions and resources 11 Functions and coding 12 General coding

Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs.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. Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: • Understand and implement AI agent behavior patterns • Design and deploy production-ready intelligent agents • Leverage the OpenAI Assistants API and complementary tools • Implement robust knowledge management and memory systems • Create self-improving agents with feedback loops • Orchestrate collaborative multi-agent systems • Enhance agents with speech and vision capabilities You won't find toy examples or fragile assistants that require constant supervision. AI Agents in Action teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Most production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems. About the book In AI Agents in Action, you’ll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You’ll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you’ll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI. What's inside • Knowledge management and memory systems • Feedback loops for continuous agent learning • Collaborative multi-agent systems • Speech and computer vision About the reader For intermediate Python programmers. About the author Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning. Table of Contents 1 Introduction to agents and their world 2 Harnessing the power of large language models 3 Engaging GPT assistants 4 Exploring multi-agent systems 5 Empowering agents with actions 6 Building autonomous assistants 7 Assembling and using an agent platform 8 Understanding agent memory and knowledge 9 Mastering agent prompts with prompt flow 10 Agent reasoning and evaluation 11 Agent planning and feedback A Accessing OpenAI large language models B Python development environment

How do you know what might have happened, had you done things differently? Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.In Causal AI you will learn how to: Build causal reinforcement learning algorithms Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro Compare and contrast statistical and econometric methods for causal inference Set up algorithms for attribution, credit assignment, and explanation Convert domain expertise into explainable causal models Causal AI is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Causal machine learning is a major milestone in machine learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars. About the book Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you. About the reader For data scientists and machine learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python. About the author Robert Ness is a leading researcher in causal AI at Microsoft Research. He is a contributor to open-source causal inference packages such as Python’s DoWhy and R’s bnlearn.

"Learn to build standout line-of-business applications using Microsoft's .NET Framework, the premier platform for enterprise business development. Based on the bestselling .NET Core in Action, the new .NET in Action, Second Edition has been completely rewritten and updated by original author Dustin Metzgar--an industry veteran who helped develop both the original .NET Framework and .NET Core."-- Publisher provided description.

Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity. In Machine Learning System Design: With end-to-end examples you will learn: • The big picture of machine learning system design • Analyzing a problem space to identify the optimal ML solution • Ace ML system design interviews • Selecting appropriate metrics and evaluation criteria • Prioritizing tasks at different stages of ML system design • Solving dataset-related problems with data gathering, error analysis, and feature engineering • Recognizing common pitfalls in ML system development • Designing ML systems to be lean, maintainable, and extensible over time Authors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Designing and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in. About the book Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips. What's inside • Metrics and evaluation criteria • Solve common dataset problems • Common pitfalls in ML system development • ML system design interview tips About the reader For readers who know the basics of software engineering and machine learning. Examples in Python. About the author Valerii Babushkin is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. Arseny Kravchenko is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental. Table of Contents Part 1 1 Essentials of machine learning system design 2 Is there a problem? 3 Preliminary research 4 Design document Part 2 5 Loss functions and metrics 6 Gathering datasets 7 Validation schemas 8 Baseline solution Part 3 9 Error analysis 10 Training pipelines 11 Features and feature engineering 12 Measuring and reporting results Part 4 13 Integration 14 Monitoring and reliability 15 Serving and inference optimization 16 Ownership and maintenance

Build resilient and scalable, cloud-native enterprise Java applications using the Quarkus framework.Quarkus lets you live-reload your Java code, deliver continuous background testing, and automatically provide database instances—plus tons more productivity-boosting features! Quarkus in Action quickly gets you up to speed with Quarkus by building a real-world business application. In Quarkus in Action, you will: • Use Quarkus Dev mode to speed up and enhance Java development • Understand how to use the Dev UI to observe and troubleshoot running applications • Automatic background testing using the Continuous Testing feature • New frameworks and libraries such as Quarkus Messaging, gRPC, and GraphQL • Simplify deployment of applications into Kubernetes and OpenShift • Automatic management of remote services such as databases and message brokers via Docker containers • Set up observability for applications by using metrics, health checks and distributed tracing Quarkus in Action is written by Martin Štefanko and Jan Martiška, Red Hat engineers who are both active contributors to the Quarkus project. In it, you’ll learn how Quarkus works and how you can integrate it into your stack for more productive Java development. Discover what makes Quarkus different from classic enterprise Java frameworks, how Quarkus streamlines creating cloud-native applications, and makes deployment easy. Foreword by Markus Eisele. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Choose a Java framework that’s as modern as your applications! Quarkus is a cloud-first framework designed for speed and cost optimization. It’s Kubernetes-aware by default and includes amazing productivity features like live reloading, continuous testing, and a developer-friendly UI that lets you code fluidly without tedious setup. About the book Quarkus in Action provides a carefully designed learning path through Quarkus’ key features and use cases. You’ll learn hands-on by implementing a working car rental application with a cloud-native microservices design that includes Kubernetes, SQL and NoSQL databases, messaging, and observability. Along the way, you’ll learn how Quarkus simplifies deployment on cloud platforms like OpenShift. What's inside • Speed up development with Quarkus Dev mode • Troubleshoot running apps with Dev UI • Continuous testing in the background • Automatic startup of development databases About the reader For intermediate Java developers who have experience deve- loping server-side Java applications. About the author Martin Štefanko and Jan Martiška are Red Hat engineers and active contributors to the Quarkus project. Table of Contents Part 1 1 What is Quarkus? 2 Your first Quarkus application 3 Enhancing developer productivity with Quarkus Part 2 4 Handling communications 5 Testing Quarkus applications 6 Exposing and securing web applications 7 Database access 8 Reactive programming 9 Quarkus messaging Part 3 10 Cloud-native application patterns 11 Quarkus applications in the cloud 12 Custom Quarkus extensions A Alternative languages and build tooling B Tools installations C Alternatives for developing frontend applications with Quarkus

Supercharge your applications with the ultimate guide to asynchronous and multithreaded programming in C#!C# Concurrency teaches you how to write effective multithreaded and asynchronous software in C#. Practical techniques, real-world examples, and useful code samples cut through the confusion around async/await and help you write rapid, reliable, and bug-free code. In C# Concurrency: Asynchronous and Multithreaded Programming you’ll learn how to: • Take full advantage of async/await • Write bug-free multithreaded code every time • Create multithreaded code that delivers real performance improvements • Grok C# and .NET multithreading and asynchronous primitives • Know when to use concurrency techniques—and when not to use them! In C# Concurrency Nir Dobovizki, a seasoned C# veteran with over 30 years of high-performance programming experience, shares his deep knowledge and expert techniques. Say goodbye to frustrating pitfalls and impossible-to-find bugs that slow down your applications. Nir's careful approach will teach you how to navigate these challenges with ease, allowing you to achieve lightning-fast performance like never before! Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Asynchronous and multithreaded programs can perform multiple tasks simultaneously without losing speed or reliability. But getting concurrency right can challenge even experienced developers. This practical book teaches you to deliver concurrent C# apps that are lighting fast and free of the deadlocks and other synchronization issues that undermine performance and take forever to find. About the book C# Concurrency equips programmers with a comprehensive understanding of multithreading and asynchronous programming, focusing on the practical use of the C# async-await feature to simplify asynchronous tasks. It teaches how to avoid common pitfalls, addresses classic multithreading issues like deadlocks and race conditions, and advanced topics such as controlling thread of execution and using thread-safe collections. What's inside • .NET multithreading and asynchronous primitives • When to use concurrency techniques—and when not to! • Confidently use async/await About the reader For experienced C# programmers. No knowledge of asynchro- nous programming required. About the author Nir Dobovizki is a senior software architect and consultant who has worked on concurrent and asynchronous systems since the late 90s. Table of Contents 1 Asynchronous programming and multithreading 2 The compiler rewrites your code 3 The async and await keywords 4 Multithreading basics 5 async/await and multithreading 6 When to use async/await 7 Classic multithreading pitfalls and how to avoid them Part 2 8 Processing a sequence of items in the background 9 Canceling background tasks 10 Await your own events 11 Controlling on which thread your asynchronous code runs 12 Exceptions and async/await 13 Thread-safe collections 14 Generating collections asynchronously/await foreach and IAsyncEnumerable

"DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you'll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you'll learn everything you need to get the most out of DuckDB--all through hands-on examples."

Tested and pragmatic methods for writing blogs, articles, and other technical pieces that stand out from the crowd!