Big data
MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
Ross Brigoli, Faisal Masood
MLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more.With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform.As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models.Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.
Anubhav Singh, Rimjhim Bhadani
Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more.With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment.By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.
Anubhav Singh, Rimjhim Bhadani
Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more.With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment.By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.
Naveen Krishnan
AI developers face a growing challenge: building intelligent systems that retain long-term memory, reason over dynamic context, and integrate safely with external tools. Model Context Protocol for LLMs provides a modern solution—offering an open, modular architecture to construct scalable LLM agents with structured context exchange. This book equips you with a complete hands-on journey to MCP. You’ll implement the protocol’s key components—resource providers, tool providers, and gateways—then use these to orchestrate agents, chain workflows, and add context-aware behavior. You’ll also learn how MCP integrates seamlessly with LangChain, AutoGen, RAG systems, and multimodal applications. Security and governance are covered in depth, helping you build privacy-compliant, threat-resistant AI apps. You’ll explore caching, async tasks, load balancing, and scaling strategies for real-world readiness. With a continuous hands-on project, MCP becomes more than a standard—it becomes a blueprint for production-grade LLM development.
Mehul Gupta, Niladri Sen
This book offers a detailed introduction to the groundbreaking field of AI agents and Model Context Protocol (MCP). The first section delves into generative AI and large language models (LLMs), exploring how these technologies power modern AI systems. From there, the book introduces the concept of AI agents—autonomous systems capable of executing tasks with varying levels of complexity. Moving into practical applications, the book focuses on Model Context Protocol, explaining its key components and how it enables effective interaction between AI and various software tools. Each chapter offers step-by-step instructions for setting up MCP servers for popular tools like Gmail, YouTube, GitHub, and more, empowering readers to automate tasks and streamline workflows. The book concludes by addressing the future of MCP, its potential risks, and how to stay safe while using these advanced technologies. Whether you're a beginner or experienced practitioner, this guide will deepen your understanding of AI and enhance your ability to leverage cutting-edge automation in daily operations.
Modelowanie danych przy użyciu Microsoft Power BI
Markus Ehrenmueller-Jensen
Samoobsługa i hurtownia danych przedsiębiorstwa z użyciem Power BI Modelowanie danych to najczęściej pomijana funkcja w Power BI Desktop, ale to właśnie ona wyróżnia Power BI spośród innych narzędzi dostępnych na rynku. Ta praktyczna książka posłuży Ci jako przycisk szybkiego przewijania do przodu dla modelowania danych przy użyciu Power BI, modelu tabelarycznego usług Analysis Services i baz danych SQL. Służy ona jako punkt wyjścia do modelowania danych, a także pomaga odświeżyć wiedzę. Autor Markus Ehrenmueller-Jensen, założyciel Savory Data, przedstawia podstawowe koncepcje modelu semantycznego Power BI wraz z praktycznymi przykładami w językach DAX, Power Query i T-SQL. Nauczysz się: - Normalizować i denormalizować dane - Stosować najlepsze praktyki dla obliczeń, flag i wskaźników, daty i godziny, wymiarów wielokrotnego stosowania i wymiarów wolnozmiennych - Pokonywać trudności związane z binningiem, budżetem, modelami zlokalizowanymi, modelami złożonymi czy tabelami zawierającymi pary kluczy i wartości - Odkrywać i rozwiązywać problemy z wydajnością za pośrednictwem modelu danych - Pracować z tabelami, relacjami, operacjami na zbiorach, postaciami normalnymi, modelowaniem wymiarowym i procesem ETL Markus Ehrenmueller-Jensen, założyciel Savory Data, od 1994 r. pracuje jako lider projektów, trener i konsultant w obszarze inżynierii danych, analityki biznesowej i danologii. Jest inżynierem oprogramowania i profesorem w HTL Leonding (wyższa szkoła techniczna), gdzie uczy baz danych i inżynierii projektów. Posiada kilka certyfikatów Microsoft, a także tytuł Microsoft Data Platform MVP. "Ta książka to wyczerpujący samouczek omawiający temat w języku, który jest łatwy do zrozumienia, a przy tym jest dogłębny, zwięzły i dokładny. Doświadczenie Markusa w zakresie modelowania danych będzie stanowić wartość dla każdego profesjonalisty pracującego z danymi przy użyciu Power BI". -Paul Turley Microsoft Data Platform MVP
Modelowanie danych z Power BI dla ekspertów analityki. Jak w pełni wykorzystać możliwości Power BI
Soheil Bakhshi, Christian Wade
Microsoft Power BI zdobył uznanie jako idealne narzędzie do analizy, modelowania i przetwarzania złożonych zbiorów danych. Dzięki niemu można bez trudu tworzyć wyrafinowane modele danych, łączyć dane z różnych źródeł, definiować relacje między nimi, a także je kształtować i zarządzać nimi. W ten sposób uzyskuje się świetną podstawę do przygotowywania raportów oraz zestawów danych na potrzeby analityki biznesowej - a to przekłada się na bardzo konkretne korzyści. Ta książka jest znakomitym wprowadzeniem do Power BI. Dzięki niej nauczysz się modelowania danych, technik definiowania relacji oraz tworzenia modeli danych. Dowiesz się też, jak prowadzić obliczenia za pomocą funkcji modelowania. Poznasz także podstawy pisania kodu w języku DAX i korzystania z nowych funkcji modelowania danych. Stopniowo przejdziesz do bardziej zaawansowanych rozwiązań, dzięki czemu Twoje modele danych sprawdzą się nawet przy bardzo złożonych zadaniach. Poszczególne zagadnienia zilustrowano praktycznymi przykładami, które pozwolą Ci zrozumieć, jak bardzo przydatne w pokonywaniu wyzwań biznesowych są zoptymalizowane modele danych. W książce między innymi: korzystanie z wirtualnych tabel i funkcji analizy czasowej języka DAX tabele wymiarów i tabele faktów oraz ich implementacja w edytorze Power Query przygotowywanie danych do budowy schematu gwiazdy najlepsze metody przygotowywania i modelowania danych różne koncepcje modelowania danych i zmniejszania poziomu złożoności modelu Optymalny model danych - oto prawdziwa inteligencja biznesowa!
V Naresh Kumar, Prashant Shindgikar
The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools.This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Modern R Programming Cookbook. Recipes to simplify your statistical applications
Jaynal Abedin
R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. The objective of this book is to show how to work with different programming aspects of R. The emerging R developers and data science could have very good programming knowledge but might have limited understanding about R syntax and semantics. Our book will be a platform develop practical solution out of real world problem in scalable fashion and with very good understanding. You will work with various versions of R libraries that are essential for scalable data science solutions. You will learn to work with Input / Output issues when working with relatively larger dataset. At the end of this book readers will also learn how to work with databases from within R and also what and how meta programming helps in developing applications.