Sagemaker Deployment And Development
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Sagemaker Deployment And Development
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-16
Sagemaker Deployment And Development written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-16 with Computers categories.
"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.
Amazon Sagemaker Best Practices
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Author : Sireesha Muppala
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-24
Amazon Sagemaker Best Practices written by Sireesha Muppala and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-24 with Computers categories.
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 capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows. What you will learnPerform data bias detection with AWS Data Wrangler and SageMaker ClarifySpeed up data processing with SageMaker Feature StoreOvercome labeling bias with SageMaker Ground TruthImprove training time with the monitoring and profiling capabilities of SageMaker DebuggerAddress the challenge of model deployment automation with CI/CD using the SageMaker model registryExplore SageMaker Neo for model optimizationImplement data and model quality monitoring with Amazon Model MonitorImprove training time and reduce costs with SageMaker data and model parallelismWho this book is for This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book.
Data Science On Aws
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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07
Data Science On Aws written by Chris Fregly and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Computers categories.
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Accelerate Deep Learning Workloads With Amazon Sagemaker
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Author : Vadim Dabravolski
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-28
Accelerate Deep Learning Workloads With Amazon Sagemaker written by Vadim Dabravolski and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-28 with Computers categories.
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.
Pragmatic Ai
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Author : Noah Gift
language : en
Publisher: Addison-Wesley Professional
Release Date : 2018-07-12
Pragmatic Ai written by Noah Gift and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-12 with Computers categories.
Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Aws Certified Cloud Developer Associate
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26
Aws Certified Cloud Developer Associate written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Computers categories.
Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
Mastering Machine Learning On Aws
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Author : Dr. Saket S.R. Mengle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Mastering Machine Learning On Aws written by Dr. Saket S.R. Mengle and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Computers categories.
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Discovering Knowledge In Data
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Author : Daniel T. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2005-01-28
Discovering Knowledge In Data written by Daniel T. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-28 with Computers categories.
Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Sagemaker Essentials
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-31
Sagemaker Essentials written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-31 with Computers categories.
"SageMaker Essentials" "SageMaker Essentials" offers a comprehensive guide to mastering Amazon SageMaker, the leading platform for machine learning at scale. This authoritative resource meticulously explores the platform’s architecture, seamlessly guiding readers through elastic infrastructure management, secure data integration, CI/CD pipeline integration, and best practices for leveraging SageMaker Studio and modern SDKs. Emphasizing enterprise needs, the book provides strategies for cost optimization, robust access management, and sustainable machine learning solutions, making it indispensable for organizations seeking operational efficiency in cloud-based AI deployments. With a keen focus on advanced data preparation, readers learn how to automate data wrangling, engineer reusable transformation pipelines, and proactively monitor data quality and drift. The book also delves into complex model training scenarios, such as distributed and multi-node training, hyperparameter optimization, and interactive experimentation, all while maintaining strict budgeting and resource usage control. The end-to-end lifecycle of machine learning, from data processing and labeling with Ground Truth to robust deployment strategies—including real-time, batch, and serverless inference—is covered with practical patterns and production-targeted guidance. Equipped for the demands of modern MLOps, "SageMaker Essentials" details the automation of ML pipelines, advanced monitoring and observability with CloudWatch, and compliance-driven security, governance, and auditability frameworks. Readers will benefit from chapters on hybrid architectures, event-driven workflows, federated learning, and extensibility with open-source and SaaS integrations. Detailed coverage of incident detection, automated remediation, and cost and environmental considerations round out this essential reference for data scientists, ML engineers, architects, and technology leaders committed to scaling secure, compliant, and efficient AI systems on AWS.
Cloud Based Machine Learning Practical Guide To Deploying Ai Models In The Cloud
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Author : Hemanth Volikatla
language : en
Publisher: RK Publication
Release Date : 2024-05-15
Cloud Based Machine Learning Practical Guide To Deploying Ai Models In The Cloud written by Hemanth Volikatla and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Computers categories.
Cloud-Based Machine Learning – Practical Guide to Deploying AI Models in the Cloud is a comprehensive resource designed to help professionals and enthusiasts harness the power of cloud platforms for AI deployment. It's key concepts, tools, and techniques for building, training, and deploying machine learning models using services like AWS, Azure, and Google Cloud. With practical examples, step-by-step instructions, and best practices, this guide empowers readers to scale AI solutions efficiently, ensuring robust performance and seamless integration into real-world applications. Perfect for beginners and experts aiming to advance their skills in cloud-based AI technologies.