Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Amazon SageMaker Best Practices
Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key FeaturesLearn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in productionAutomate end-to-end machine learning workflows with Amazon SageMaker and related AWSDesign, architect, and operate machine learning workloads in the AWS CloudBook Description Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabili...
Learning Agile
Learning Agile is a comprehensive guide to the most popular agile methods, written in a light and engaging style that makes it easy for you to learn. Agile has revolutionized the way teams approach software development, but with dozens of agile methodologies to choose from, the decision to "go agile" can be tricky. This practical book helps you sort it out, first by grounding you in agile’s underlying principles, then by describing four specific—and well-used—agile methods: Scrum, extreme programming (XP), Lean, and Kanban. Each method focuses on a different area of development, but they all aim to change your team’s mindset—from individuals who simply follow a plan to a cohesive g...
Generative AI on AWS
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll e...
Accelerate Deep Learning Workloads with Amazon SageMaker
Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance. Key FeaturesExplore key Amazon SageMaker capabilities in the context of deep learningTrain and deploy deep learning models using SageMaker managed capabilities and optimize your deep learning workloadsCover in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMakerBook Description Over the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of prac...
Natural Language Processing with AWS AI Services
Work through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI services Key FeaturesGet to grips with AWS AI services for NLP and find out how to use them to gain strategic insightsRun Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomesUnderstand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2IBook Description Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP...
The Machine Learning Solutions Architect Handbook
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries...
Machine Learning with Amazon SageMaker Cookbook
A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker Key FeaturesPerform ML experiments with built-in and custom algorithms in SageMakerExplore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learnUse the different features and capabilities of SageMaker to automate relevant ML processesBook Description Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML p...
IA generativa en AWS
Las empresas de hoy en día avanzan rápidamente para integrar la inteligencia artificial generativa en sus productos y servicios. Hay mucha agitación (y también malentendidos) sobre el impacto y la promesa de esta tecnología. Con este libro, Chris Fregly, Antje Barth y Shelbee Eigenbrode, de Amazon Web Services, le ayudarán a encontrar formas prácticas de usar esta tecnología tan nueva y atractiva. Gracias a IA generativa en AWS, descubrirá el ciclo de vida de los proyectos de IA generativa, que incluye la definición de casos de uso, la selección y el ajuste de los modelos, la generación mejorada por recuperación, el aprendizaje por refuerzo a partir de la retroalimentación huma...
Getting Started with Amazon SageMaker Studio
Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code Key FeaturesUnderstand the ML lifecycle in the cloud and its development on Amazon SageMaker StudioLearn to apply SageMaker features in SageMaker Studio for ML use casesScale and operationalize the ML lifecycle effectively using SageMaker StudioBook Description Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), ...
