Microservices with Go
This book covers the key benefits and common issues of microservices, helping you understand the problems microservice architecture helps to solve, the issues it usually introduces, and the ways to tackle them. You'll start by learning about the importance of using the right principles and standards in order to achieve the key benefits of microservice architecture. The following chapters will explain why the Go programming language is one of the most popular languages for microservice development and lay down the foundations for the next chapters of the book. You'll explore the foundational aspects of Go microservice development including service scaffolding, service discovery, data serialization, synchronous and asynchronous communication, deployment, and testing. After covering the development aspects, you'll progress to maintenance and reliability topics. The last part focuses on more advanced topics of Go microservice development including system reliability, observability, main ...
This book covers the key benefits and common issues of microservices, helping you understand the problems microservice architecture helps to solve, the issues it usually introduces, and the ways to tackle them. You'll start by learning about the importance of using the right principles and standards in order to achieve the key benefits of microservice architecture. The following chapters will explain why the Go programming language is one of the most popular languages for microservice development and lay down the foundations for the next chapters of the book. You'll explore the foundational aspects of Go microservice development including service scaffolding, service discovery, data serialization, synchronous and asynchronous communication, deployment, and testing. After covering the development aspects, you'll progress to maintenance and reliability topics. The last part focuses on more advanced topics of Go microservice development including system reliability, observability, main ...
Web Development with Julia and Genie
Julia's high-performance and scalability characteristics and its extensive number of packages for visualizing data make it an excellent fit for developing web apps, web services, and web dashboards. The two parts of this book provide complete coverage to build your skills in web development. First, you'll refresh your knowledge of the main concepts in Julia that will further be used in web development. Then, you'll use Julia's standard web packages and examine how the building blocks of the web such as TCP-IP, web sockets, HTTP protocol, and so on are implemented in Julia's standard library. Each topic is discussed and developed into code that you can apply in new projects, from static websites to dashboards. You'll also understand how to choose the right Julia framework for a project. The second part of the book talks about the Genie framework. You'll learn how to build a traditional to do app following the MVC design pattern. Next, you'll add a REST API to this project, including ...
Julia's high-performance and scalability characteristics and its extensive number of packages for visualizing data make it an excellent fit for developing web apps, web services, and web dashboards. The two parts of this book provide complete coverage to build your skills in web development. First, you'll refresh your knowledge of the main concepts in Julia that will further be used in web development. Then, you'll use Julia's standard web packages and examine how the building blocks of the web such as TCP-IP, web sockets, HTTP protocol, and so on are implemented in Julia's standard library. Each topic is discussed and developed into code that you can apply in new projects, from static websites to dashboards. You'll also understand how to choose the right Julia framework for a project. The second part of the book talks about the Genie framework. You'll learn how to build a traditional to do app following the MVC design pattern. Next, you'll add a REST API to this project, including ...
Applying Math with Python, 2nd Edition
The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, y ...
The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, y ...
Redis For Dummies
NoSQL is a modern data persistence storage paradigm that provides data persistence for environments where high performance is a primary requirement. Within NoSQL, data is stored in such a way as to make both writing and reading quite fast, even under heavy load. Redis and Redis Enterprise are market-leading, multi-model NoSQL databases that bring NoSQL to organizations both big and small. Redis is open source, and Redis Enterprise software adds several enhancements that are important to the enterprise customer. Redis Enterprise Cloud enables Redis Enterprise deployments on popular cloud providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. This book provides a starting point for those new to NoSQL and those who have heard about NoSQL but would like to see how it might be used in their organization. The book serves multiple audiences, with chapters geared towards managers and chapters specifically for developers. You don't need to read this book from fron ...
NoSQL is a modern data persistence storage paradigm that provides data persistence for environments where high performance is a primary requirement. Within NoSQL, data is stored in such a way as to make both writing and reading quite fast, even under heavy load. Redis and Redis Enterprise are market-leading, multi-model NoSQL databases that bring NoSQL to organizations both big and small. Redis is open source, and Redis Enterprise software adds several enhancements that are important to the enterprise customer. Redis Enterprise Cloud enables Redis Enterprise deployments on popular cloud providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. This book provides a starting point for those new to NoSQL and those who have heard about NoSQL but would like to see how it might be used in their organization. The book serves multiple audiences, with chapters geared towards managers and chapters specifically for developers. You don't need to read this book from fron ...
Designing Event-Driven Systems
Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but as this practical ebook demonstrates, they provide a more holistic and compelling approach when applied together. Author Ben Stopford explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems. You'll learn how to apply patterns including Event Sourcing and CQRS, and how to build multi-team systems with microservices and SOA using patterns such as "inside out databases" and "event streams as a source of truth." These approaches provide a unique foundation for how these large, autonomous service ecosystem ...
Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but as this practical ebook demonstrates, they provide a more holistic and compelling approach when applied together. Author Ben Stopford explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems. You'll learn how to apply patterns including Event Sourcing and CQRS, and how to build multi-team systems with microservices and SOA using patterns such as "inside out databases" and "event streams as a source of truth." These approaches provide a unique foundation for how these large, autonomous service ecosystem ...
Reliable Machine Learning
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. ...
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. ...
Productionizing AI
This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app. From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions. The b ...
This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app. From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions. The b ...
ASP.NET Core APIs Succinctly
APIs are such a critical part of software that "API-first" has become a popular approach to development, one where the API is designed before the app itself is written. Without a well-designed API, your app is isolated from the greater app ecosystem. In ASP.NET Core APIs Succinctly, author Dirk Strauss guides you through designing your own REST API, using it to return and modify data, and versioning it as you continue to develop it. ...
APIs are such a critical part of software that "API-first" has become a popular approach to development, one where the API is designed before the app itself is written. Without a well-designed API, your app is isolated from the greater app ecosystem. In ASP.NET Core APIs Succinctly, author Dirk Strauss guides you through designing your own REST API, using it to return and modify data, and versioning it as you continue to develop it. ...
Generative Deep Learning, 2nd Edition
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...
Machine Learning for High-Risk Applications
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI - a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security; Lea ...
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI - a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security; Lea ...