Sagemaker Deployment And Development
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SageMaker Deployment and Development
"SageMaker Deployment and Development" "SageMaker Deployment and Development" is an authoritative guide to mastering the full spectrum of machine learning (ML) workflows using AWS SageMaker. This comprehensive book dives deep into SageMaker’s modular architecture, unraveling the intricacies of its core components such as Studio, Training, Inference, Processing, and Feature Store. Readers acquire actionable insights into managing containerized environments, integrating with the broader AWS ecosystem, and architecting data flows for scalability, security, and efficiency. Advanced discussions explore distributed computing strategies, cost optimization, and high-performance resource management—enabling ML professionals to build robust, enterprise-grade deployments. The volume thoroughly addresses advanced model development workflows, guiding practitioners from experiment tracking and custom algorithm containers to hyperparameter optimization and versioned feature engineering. Readers will discover best practices for reproducibility, environment management, and multi-framework integration with leading ML libraries such as PyTorch, TensorFlow, and Scikit-learn. Rich coverage of data engineering tackles automated pipelines, batch and streaming data integration, and seamless connections to data lakes and warehouses, all underpinned by stringent quality, validation, and auditability principles. Recognizing the demands of operating ML in production, the book dedicates extensive chapters to security, compliance, and governance, offering practical solutions for regulated industries and multi-tenant environments. It surveys the state of MLOps with hands-on techniques for CI/CD, automated testing, and controlled model promotion. Techniques for large-scale, distributed training, inference endpoint management, monitoring, and drift detection are paired with insights into extensibility, custom integrations, and future trends. Whether you’re a data scientist, ML engineer, or cloud architect, "SageMaker Deployment and Development" equips you with the knowledge and skills to deliver secure, scalable, and future-proof ML solutions on AWS.
Accelerate Deep Learning Workloads with Amazon SageMaker
Author: Vadim Dabravolski
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-10-28
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 practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep-learning tasks, such as computer vision and natural language processing. You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open-source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads. By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker. What you will learnCover key capabilities of Amazon SageMaker relevant to deep learning workloadsOrganize SageMaker development environmentPrepare and manage datasets for deep learning trainingDesign, debug, and implement the efficient training of deep learning modelsDeploy, monitor, and optimize the serving of DL modelsWho this book is for This book is relevant for ML engineers who work on deep learning model development and training, and for Solutions Architects who design and optimize end-to-end deep learning workloads. It assumes familiarity with the Python ecosystem, principles of Machine Learning and Deep Learning, and basic knowledge of the AWS cloud.
AWS Certified Cloud Developer Associate
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