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Author: Chris Fregly
Publisher: "O'Reilly Media, Inc."
ISBN: 1492079367
Category : Computers
Languages : en
Pages : 524
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Book Description
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
Author: Chris Fregly
Publisher: "O'Reilly Media, Inc."
ISBN: 1492079367
Category : Computers
Languages : en
Pages : 524
Get Book Here
Book Description
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
Author: Sireesha Muppala
Publisher: Packt Publishing Ltd
ISBN: 1801077762
Category : Computers
Languages : en
Pages : 348
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Book Description
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.
Author: David Ping
Publisher: Packt Publishing Ltd
ISBN: 1801070415
Category : Computers
Languages : en
Pages : 442
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Book Description
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, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
Author: Venere Ferraro
Publisher: Springer Nature
ISBN: 3031719115
Category :
Languages : en
Pages : 150
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Book Description
Author: Vinod Kumar Khanna
Publisher: CRC Press
ISBN: 1040156746
Category : Science
Languages : en
Pages : 373
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Book Description
This book introduces the basics of the Internet of Things (IoT) and explores the foundational role of sensors in IoT applications. The IoT is a network of devices and objects: sensors, actuators, hardware, software, human beings, domestic appliances, health monitoring equipment, and other things connected to the internet, which is designed to operate in a coordinated fashion to receive, process, and interpret signals and take appropriate action. It provides a seamless real-time interface between the physical and digital worlds by integrating sensors with networking, computation, and actuation facilities. This book sketches a perspective of the IoT with sensors as the focus of attention. Diverse applications of the IoT that are destined to make an impact on our everyday lives in the near future are discussed. It presents a comprehensive overview of the most recent sensor technologies used in the IoT to keep the reader abreast of the current advances at the frontiers of knowledge. The book will cater to student and professional audiences, and will be useful for postgraduate and Ph.D. students studying physics, engineering, and computer science as well as researchers, engineers, and industrial workers engaged in this fast-progressing field. Key Features: • Explains the basic concepts and important terms of ‘Internet of Things’ in simple language • Provides an up-to-date coverage of the key sensors used in IoT applications • Explores IoT applications in smart cities, smart agriculture, smart factory, and many more
Author: Diego Pacheco
Publisher: BPB Publications
ISBN: 9355519532
Category : Computers
Languages : en
Pages : 591
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Book Description
Long path to better systems that last longer and make engineers and customers happier KEY FEATURES ● Guidance, trade-offs analysis, principles, and insights on understanding complex microservices and monoliths problems and solutions at scale. ● In-depth coverage of anti-patterns, allowing the reader to avoid pitfalls and understand how to handle architecture at scale better. ● Concepts and lessons learned through experience in performing code and data migration at scale with complex architectures. Best usage of new technology using the right architecture principles. DESCRIPTION This book is a comprehensive guide to designing scalable and maintainable software written by an expert. It covers the principles, patterns, anti-patterns, trade-offs, and concepts that software developers and architects need to understand to design software that is both scalable and maintainable. The book begins by introducing the concept of monoliths and discussing the challenges associated with scaling and maintaining them. It then covers several anti-patterns that can lead to these challenges, such as lack of isolation and internal shared libraries. The next section of the book focuses on the principles of good software design, such as loose coupling and encapsulation. It also covers several software architecture patterns that can be used to design scalable and maintainable monoliths, such as the layered architecture pattern and the microservices pattern. The final section of the book guides how to migrate monoliths to distributed systems. It also covers how to test and deploy distributed systems effectively. WHAT YOU WILL LEARN ● Understand the challenges of monoliths and the common anti-patterns that lead to them. ● Learn the principles of good software design, such as loose coupling and encapsulation. ● Discover software architecture patterns that can be used to design scalable and maintainable monoliths. ● Get guidance on how to migrate monoliths to distributed systems. ● Learn how to test and deploy distributed systems effectively. WHO THIS BOOK IS FOR This book is for software developers, architects, system architects, DevOps engineers, site reliability engineers, and anyone who wants to learn about the principles and practices of modernizing software architectures. The book is especially relevant for those who are working with legacy systems or want to design new systems that are scalable, resilient, and maintainable. TABLE OF CONTENTS 1. What’s Wrong with Monoliths? 2. Anti-Patterns: Lack of Isolation 3. Anti-Patterns: Distributed Monoliths 4. Anti-Patterns: Internal Shared Libraries 5. Assessments 6. Principles of Proper Services 7. Proper Service Testing 8. Embracing New Technology 9. Code Migrations 10. Data Migrations 11. Epilogue
Author: Wil Hoverd
Publisher: Massey University Press
ISBN: 1991016638
Category : Political Science
Languages : en
Pages : 365
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Book Description
Increasing US&– China tensions, Russia' s invasion of Ukraine, disruptions to supply chains and maritime trade, right-wing extremism, gangs and the drug trade . . . The international and domestic security environment is dynamic and fraught. In State of Threat, local and international academics and sector experts discuss the issues facing New Zealand across defence, diplomacy, intelligence, policy, trade and border management.This timely and up-to-date analysis of New Zealand' s most important security issues is a must-read for policy analysts, those working in risk management and industry leaders across all sectors of the economy.
Author: Noah Gift
Publisher: "O'Reilly Media, Inc."
ISBN: 1098102967
Category : Computers
Languages : en
Pages : 466
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Book Description
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Author: Suvoraj Biswas
Publisher: Packt Publishing Ltd
ISBN: 1836202903
Category : Computers
Languages : en
Pages : 114
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Book Description
Elevate your AI projects with our course on Enterprise Generative AI using AWS's Well-Architected Framework, paving the way for innovation and efficiency Key Features Learn to secure AI environments Achieve excellence in AI architecture Implement AI with AWS solutions Book DescriptionThe course begins with an insightful introduction to the burgeoning field of Generative AI, laying down a robust framework for understanding its applications within the AWS ecosystem. The course focuses on meticulously detailing the five pillars of the AWS Well-Architected Framework—Operational Excellence, Security, Compliance, Reliability, and Cost Optimization. Each module is crafted to provide you with a comprehensive understanding of these essential areas, integrating Generative AI technologies. You'll learn how to navigate the complexities of securing AI systems, ensuring they comply with legal and regulatory standards, and designing them for unparalleled reliability. Practical sessions on cost optimization strategies for AI projects will empower you to deliver value without compromising on performance or scalability. Furthermore, the course delves into System Architecture Excellence, emphasizing the importance of robust design principles in creating effective Generative AI solutions. The course wraps up by offering a forward-looking perspective on the Common Architectural Pattern for FM/LLM Integration & Adoption within the AWS framework. You'll gain hands-on experience with AWS solutions specifically tailored for Generative AI applications, including Lambda, API Gateway, and DynamoDB, among others.What you will learn Apply Operational Excellence in AI Secure Generative AI implementations Navigate compliance in AI solutions Ensure reliability in AI systems Optimize costs for AI projects Integrate FM/LLM with AWS solutions Who this book is for This course is designed for IT professionals, solutions architects, and DevOps engineers looking to specialize in Generative AI. A foundational understanding of AWS and cloud computing is beneficial.
Author: Adnan Masood
Publisher: Packt Publishing Ltd
ISBN: 1800565526
Category : Computers
Languages : en
Pages : 312
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Book Description
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
