Engineering Data Mesh in Azure Cloud. Implement data mesh using Microsoft Azure's Cloud Adoption Framework

Aniruddha Deswandikar

Decentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help.The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud.The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI).By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.

Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale

Emmanuel Raj

Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you’ll apply the knowledge you’ve gained to build real-world projects.By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.

English 4 IT. Praktyczny kurs języka angielskiego dla specjalistów IT i nie tylko

Beata Błaszczyk

Poszerz zasób słownictwa z branży IT i naucz się poprawnie je wykorzystywać! Obecnie język angielski jest nieodłącznym elementem świata IT. Większość producentów oprogramowania oraz twórców metodyk i standardów w branży IT udostępnia materiały wyłącznie w tym języku. Ponadto wiele projektów informatycznych jest realizowanych we współpracy z ekspertami pochodzącymi z różnych krajów. Komunikacja odbywa się wówczas przeważnie także w języku angielskim. Jeśli więc jesteś specjalistą z branży IT, funkcjonującym na co dzień w środowisku anglojęzycznym, możesz z kolei nie czuć się w pełni swobodnie wypowiadając się w tym języku w każdym obszarze tak szerokiej dziedziny wiedzy. Jeśli chcesz zatem: poznać lub poszerzyć specjalistyczne słownictwo stosowane na co dzień w branży IT, nauczyć się poprawnie je wykorzystywać i profesjonalnie wypowiadać się podczas spotkań, konferencji branżowych i codziennej pracy z klientem, zrozumieć zaawansowane reguły gramatyczne lub odświeżyć wiedzę na ten temat, by zabłysnąć podczas formalnych spotkań lub jako autor profesjonalnej dokumentacji w języku angielskim, zacząć posługiwać się prawidłowymi polskimi terminami zamiast kalkami językowymi, Koniecznie sięgnij po tę książkę! Znajdziesz tu profesjonalne angielskie teksty dotyczące różnych obszarów IT: baz danych, big data, sieci komputerowych, metod wytwarzania oprogramowania i wielu innych. Każdy z nich został opatrzony słowniczkiem zawierającym tłumaczenia na język polski zarówno terminów specjalistycznych, jak i tych z języka ogólnego. Ponadto w każdym rozdziale omówione zostały różne zagadnienia gramatyczne wraz z przykładami ich zastosowania w języku informatyki. Na końcu każdego rozdziału znajdziesz ćwiczenia, które pomogą Ci utrwalić poznane słownictwo i reguły gramatyczne. Dzięki temu opanujesz najważniejsze pojęcia, odkryjesz, w jakich kontekstach występują, i zaczniesz stosować je w praktyce. What is Information Technology? Databases How well do you know your computer? Computer networks What's so big about big data? Business Intelligence Data mining Software licensing Software development methodologies The Internet and the World Wide Web Data governance Software testing Revise and expand Your knowledge!

Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Matt Benatan, Jochem Gietema, Marian Schneider

Deep learning is revolutionizing our lives, impacting content recommendations and playing a key role in mission- and safety-critical applications. Yet, typical deep learning methods lack awareness about uncertainty. Bayesian deep learning offers solutions based on approximate Bayesian inference, enhancing the robustness of deep learning systems by indicating how confident they are in their predictions. This book will guide you in incorporating model predictions within your applications with care.Starting with an introduction to the rapidly growing field of uncertainty-aware deep learning, you’ll discover the importance of uncertainty estimation in robust machine learning systems. You’ll then explore a variety of popular Bayesian deep learning methods and understand how to implement them through practical Python examples covering a range of application scenarios.By the end of this book, you’ll embrace the power of Bayesian deep learning and unlock a new level of confidence in your models for safer, more robust deep learning systems.

Enhancing Productivity with Notion. Save time on projects by supercharging your productivity with Notion's powerful features and templates

Danny Hatcher

Notion is note-taking, wiki management, and task and project management software that gives you control of your working environment. By creating pages using a variety of block types and database styles, the possibilities are endless. This book will help you overcome challenges in managing large projects using Notion and creating documents using real-time updates with a great deal of flexibility, allowing everyone to have a customized view as per their needs.With this book, you'll become well-versed with Notion, where you’ll learn how to access, create, and change a workspace utilizing all the blocks, database views and properties, and advanced features and functions. From a table or list view to a Kanban board or gallery view, you'll get to explore several database options available in Notion. You’ll learn the skills to build pages, customize the aesthetics, create templates, and store, organize, and surface information through databases, all while building a system and workflow that works for you in your context. The book will also demonstrate how to import and export information and combine Notion with other tools using the API.By the end of this book, you’ll be able to create a task management system, project management system, or any other system while combining it with other tools that speed up your work for better efficiency.

Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python

Dipayan Sarkar, Vijayalakshmi Natarajan

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

Enterprise Agility. Being Agile in a Changing World

Sunil Mundra

The biggest challenge enterprises face today is dealing with fast-paced change in all spheres of business. Enterprise Agility shows how an enterprise can address this challenge head on and thrive in the dynamic environment. Avoiding the mechanistic construction of existing enterprises that focus on predictability and certainty, Enterprise Agility delivers practical advice for responding and adapting to the scale and accelerating pace of disruptive change in the business environment.Agility is a fundamental shift in thinking about how enterprises work to effectively deal with disruptive changes in the business environment. The core belief underlying agility is that enterprises are open and living systems. These living systems, also known as complex adaptive systems (CAS), are ideally suited to deal with change very effectively.Agility is to enterprises what health is to humans. There are some foundational principles that can be broadly applied, but the definition of healthy is very specific to each individual. Enterprise Agility takes a similar approach with regard to agility: it suggests foundational practices to improve the overall health of the body—culture, mindset, and leadership—and the health of its various organs: people, process, governance, structure, technology, and customers. The book also suggests a practical framework to create a plan to enhance agility.

Enterprise DevOps for Architects. Leverage AIOps and DevSecOps for secure digital transformation

Jeroen Mulder

Digital transformation is the new paradigm in enterprises, but the big question remains: is the enterprise ready for transformation using native technology embedded in Agile/DevOps? With this book, you'll see how to design, implement, and integrate DevOps in the enterprise architecture while keeping the Ops team on board and remaining resilient. The focus of the book is not to introduce the hundreds of different tools that are available for implementing DevOps, but instead to show you how to create a successful DevOps architecture.This book provides an architectural overview of DevOps, AIOps, and DevSecOps – the three domains that drive and accelerate digital transformation. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this DevOps book will help you to successfully integrate DevOps into enterprise architecture. You'll learn what AIOps is and what value it can bring to an enterprise. Lastly, you will learn how to integrate security principles such as zero-trust and industry security frameworks into DevOps with DevSecOps.By the end of this DevOps book, you'll be able to develop robust DevOps architectures, know which toolsets you can use for your DevOps implementation, and have a deeper understanding of next-level DevOps by implementing Site Reliability Engineering (SRE).