Название: Effective Machine Learning Teams: Best Practices for Ml Practitioners (Final)
Автор: Dаvid Таn, Аdа Lеung, Dаvid Соlls
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 402
Язык: английский
Формат: pdf (true), epub (true)
Размер: 15.1 MB, 10.1 MB

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how to overcome friction and experience flow when delivering ML solutions.

Название: Machine Learning Theory and Applications: Hands-on Use Cases with Python on Classical and Quantum Machines
Автор: Хаviеr Vаsquеs
Издательство: Wiley
Год: 2024
Страниц: 510
Язык: английский
Формат: pdf (true)
Размер: 38.9 MB

Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.

Название: Implementing MLOps in the Enterprise: A Production-First Approach
Автор: Yаrоn Наviv, Nоаh Gift
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 377
Язык: английский
Формат: epub (true)
Размер: 15.5 MB

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book is for practitioners in charge of building, managing, maintaining, and operationalizing the Data Science process end to end: the heads of Data Science, heads of ML engineering, senior data scientists, MLOps engineers, and Machine Learning engineers. These practitioners are familiar with the nooks and crannies (as well as the challenges and obstacles) of the Data cience pipeline, and they have the initial technological know-how, for example, in Python, Pandas, Sklearn, and others.

Название: Building Recommendation Systems in Python and JAX: Hands-On Production Systems at Scale (Final)
Автор: Вrуаn Вisсhоf, Несtоr Yее
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 355
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.3 MB, 10.1 MB

Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases. Modern recommendation system (often abbreviated RecSys) designs are as diverse as the domains they serve. RecSys consist of the computer software architectures to implement and execute such product goals in addition to the algorithmic components of ranking. Methods for ranking recommendions can come from traditional statistical learning algorithms, linear algebraic inspirations, geometric considerations, and, of course, gradient based methods. Just as the algorithmic methods are diverse, so too are the modeling and evaluation considerations for recommending: personalized ranking, search recommendations, sequence modeling, and the scoring for all of the above are now need-to-know for the working ML Engineer in the space of recommendation systems.

Название: Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Cataloging, Governance, Integration, Orchestration, and Consumption
Автор: Eberhard Hechler, Maryela Weihrauch, Yan (Catherine) Wu
Издательство: Apress
Год: 2023
Страниц: 440
Язык: английский
Формат: pdf (true), epub
Размер: 41.6 MB

Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance—all designed to deliver "data as a product" within hybrid cloud landscapes. This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses Artificial Intelligence (AI) and Machine Learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.

Название: Effective Machine Learning Teams: Best Practices for ML Practitioners (Fifth Early Release)
Автор: David Tan, Ada Leung
Издательство: O’Reilly Media, Inc.
Год: 2023-07-18
Страниц: 296
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.

Название: Production Engineering from DevOps to MLOps
Автор: Arnab Bose, Sebastien Donadio
Издательство: Leanpub
Год: 2023-06-29
Страниц: 124
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB

The book to bridge DevOps and MLOps. This book takes a DevOps approach to MLOps and uniquely positions how MLOps is an extension of well-established DevOps principles using real-world use cases. It leverages multiple DevOps concepts and methodologies such as CI/CD and software testing. It also demonstrates the additional concepts from MLOps such as continuous training that expands CI/CD/CT to build, operationalize and monitor ML models. This book targets three different personas. First, data engineers and DevOps engineers who manage ML data and model platforms, deploy ML model software into production and monitor them. Second, full-stack data scientists who not only build ML models but work on the end-to-end stack of the ML lifecycle starting with data ingestion to production deployment and monitoring. Third, project managers who need to understand the intricacies of the different steps in taking an ML model to production.

Название: Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps
Автор: Suhas Pote
Издательство: BPB Publications
Год: 2023
Страниц: 469
Язык: английский
Формат: pdf (true)
Размер: 38.98 MB

Deploy, manage, and scale Machine Learning models with MLOps effortlessly. ‘Machine Learning in Production’ is an attempt to decipher the path to a remarkable career in the field of MLOps. It is a comprehensive guide to managing the machine learning lifecycle from development to deployment, outlining ways in which you can deploy ML models in production. It starts off with fundamental concepts, an introduction to the ML lifecycle and MLOps, followed by comprehensive step-by-step instructions on how to develop a package for ML code from scratch that can be installed using pip. It then covers MLflow for ML life cycle management, CI/CD pipelines, and shows how to deploy ML applications on Azure, GCP, and AWS. Furthermore, it provides guidance on how to convert Python applications into Android and Windows apps, as well as how to develop ML web apps. Finally, it covers monitoring, the critical topic of machine learning attacks, and A/B testing.

Название: Streaming Data Mesh: A Model for Optimizing Real-Time Data Services (Final Release)
Автор: Нubеrt Dulеу, Stерhеn Мооnеу
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 223
Язык: английский
Формат: epub
Размер: 10.2 MB

Data lakes and warehouses have become increasingly fragile, costly, and difficult to maintain as data gets bigger and moves faster. Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services. Data mesh is one of the most popular architectures for data platforms that many are exploring today. This book will help you get a full understanding of this self-servicing data platform in a streaming context. Today, batch processing dominates all extract, transform, and load (ETL) processes in most businesses. This book will help show a different perspective of data pipelines and apply the same concepts you already understand in batch ETL, but in a streaming ETL in the context of a data mesh. This book is designed to help you understand the essential concepts around streaming data mesh—the concepts, architectures, and technologies at its core.

Название: MLOps Engineering at Scale
Автор: Carl Osipov
Издательство: Manning Publications
Год: 2022
Страниц: 344
Язык: английский
Формат: epub
Размер: 10.2 MB

MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production. To get the most value from this book, you’ll want to have existing skills in data analysis with Python and SQL, as well as have some experience with machine learning. I expect that if you are reading this book, you are interested in developing your expertise as a machine learning engineer, and you are planning to deploy your machine learning—based prototypes to production.