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Bring elasticity and innovation to Machine Learning and AI operations KEY FEATURES ● Coverage includes a wide range of AWS AI and ML services to help you speedily get fully operational with ML. ● Packed with real-world examples, practical guides, and expert data science methods for improving AI/ML education on AWS. ● Includes ready-made, purpose-built models as AI services and proven methods to adopt MLOps techniques. DESCRIPTION Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation. In this book, you'll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection. Every step of your Machine Learning and AI project's development process is optimised throughout the book by utilising Amazon's pre-made, purpose-built AI services. WHAT YOU WILL LEARN ● Learn how to build, deploy, and manage large-scale AI and ML applications on AWS. ● Get your hands dirty with AWS AI services like SageMaker, Comprehend, Rekognition, Lookout, and AutoML. ● Master data transformation, feature engineering, and model training with Amazon SageMaker modules. ● Use neural networks, distributed learning, and deep learning algorithms to improve ML models. ● Use AutoML, SageMaker Canvas, and Autopilot for Model Deployment and Evaluation. ● Acquire expertise with Amazon SageMaker Studio, Jupyter Server, and ML frameworks such as TensorFlow and MXNet. WHO THIS BOOK IS FOR Data Engineers, Data Scientists, AWS and Cloud Professionals who are comfortable with machine learning and the fundamentals of Python will find this book powerful. Familiarity with AWS would be helpful but is not required. TABLE OF CONTENTS 1. Introducing the ML Workflow 2. Hydrating the Data Lake 3. Predicting the Future With Features 4. Orchestrating the Data Continuum 5. Casting a Deeper Net (Algorithms and Neural Networks) 6. Iteration Makes Intelligence (Model Training and Tuning) 7. Let George Take Over (AutoML in Action) 8. Blue or Green (Model Deployment Strategies) 9. Wisdom at Scale with Elastic Inference 10. Adding Intelligence with Sensory Cognition 11. AI for Industrial Automation 12. Operationalized Model Assembly (MLOps and Best Practices)
Bring elasticity and innovation to Machine Learning and AI operations KEY FEATURES ● Coverage includes a wide range of AWS AI and ML services to help you speedily get fully operational with ML. ● Packed with real-world examples, practical guides, and expert data science methods for improving AI/ML education on AWS. ● Includes ready-made, purpose-built models as AI services and proven methods to adopt MLOps techniques. DESCRIPTION Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation. In this book, you'll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection. Every step of your Machine Learning and AI project's development process is optimised throughout the book by utilising Amazon's pre-made, purpose-built AI services. WHAT YOU WILL LEARN ● Learn how to build, deploy, and manage large-scale AI and ML applications on AWS. ● Get your hands dirty with AWS AI services like SageMaker, Comprehend, Rekognition, Lookout, and AutoML. ● Master data transformation, feature engineering, and model training with Amazon SageMaker modules. ● Use neural networks, distributed learning, and deep learning algorithms to improve ML models. ● Use AutoML, SageMaker Canvas, and Autopilot for Model Deployment and Evaluation. ● Acquire expertise with Amazon SageMaker Studio, Jupyter Server, and ML frameworks such as TensorFlow and MXNet. WHO THIS BOOK IS FOR Data Engineers, Data Scientists, AWS and Cloud Professionals who are comfortable with machine learning and the fundamentals of Python will find this book powerful. Familiarity with AWS would be helpful but is not required. TABLE OF CONTENTS 1. Introducing the ML Workflow 2. Hydrating the Data Lake 3. Predicting the Future With Features 4. Orchestrating the Data Continuum 5. Casting a Deeper Net (Algorithms and Neural Networks) 6. Iteration Makes Intelligence (Model Training and Tuning) 7. Let George Take Over (AutoML in Action) 8. Blue or Green (Model Deployment Strategies) 9. Wisdom at Scale with Elastic Inference 10. Adding Intelligence with Sensory Cognition 11. AI for Industrial Automation 12. Operationalized Model Assembly (MLOps and Best Practices)
Apply cloud native patterns and practices to deliver responsive, resilient, elastic, and message-driven systems with confidence Key FeaturesDiscover best practices for applying cloud native patterns to your cloud applicationsExplore ways to effectively plan resources and technology stacks for high security and fault toleranceGain insight into core architectural principles using real-world examplesBook Description Cloud computing has proven to be the most revolutionary IT development since virtualization. Cloud native architectures give you the benefit of more flexibility over legacy systems. This Learning Path teaches you everything you need to know for designing industry-grade cloud applications and efficiently migrating your business to the cloud. It begins by exploring the basic patterns that turn your database inside out to achieve massive scalability. You’ll learn how to develop cloud native architectures using microservices and serverless computing as your design principles. Then, you’ll explore ways to continuously deliver production code by implementing continuous observability in production. In the concluding chapters, you’ll learn about various public cloud architectures ranging from AWS and Azure to the Google Cloud Platform, and understand the future trends and expectations of cloud providers. By the end of this Learning Path, you’ll have learned the techniques to adopt cloud native architectures that meet your business requirements. This Learning Path includes content from the following Packt products: Cloud Native Development Patterns and Best Practices by John GilbertCloud Native Architectures by Erik Farr et al.What you will learnUnderstand the difference between cloud native and traditional architectureAutomate security controls and configuration managementMinimize risk by evolving your monolithic systems into cloud native applicationsExplore the aspects of migration, when and why to use itApply modern delivery and testing methods to continuously deliver production codeEnable massive scaling by turning your database inside outWho this book is for This Learning Path is designed for developers who want to progress into building cloud native systems and are keen to learn the patterns involved. Software architects, who are keen on designing scalable and highly available cloud native applications, will also find this Learning Path very useful. To easily grasp these concepts, you will need basic knowledge of programming and cloud computing.
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Learn and understand the need to architect cloud applications and migrate your business to cloud efficiently Key Features Understand the core design elements required to build scalable systems Plan resources and technology stacks effectively for high security and fault tolerance Explore core architectural principles using real-world examples Book Description Cloud computing has proven to be the most revolutionary IT development since virtualization. Cloud native architectures give you the benefit of more flexibility over legacy systems. To harness this, businesses need to refresh their development models and architectures when they find they don’t port to the cloud. Cloud Native Architectures demonstrates three essential components of deploying modern cloud native architectures: organizational transformation, deployment modernization, and cloud native architecture patterns. This book starts with a quick introduction to cloud native architectures that are used as a base to define and explain what cloud native architecture is and is not. You will learn what a cloud adoption framework looks like and develop cloud native architectures using microservices and serverless computing as design principles. You’ll then explore the major pillars of cloud native design including scalability, cost optimization, security, and ways to achieve operational excellence. In the concluding chapters, you will also learn about various public cloud architectures ranging from AWS and Azure to the Google Cloud Platform. By the end of this book, you will have learned the techniques to adopt cloud native architectures that meet your business requirements. You will also understand the future trends and expectations of cloud providers. What you will learn Learn the difference between cloud native and traditional architecture Explore the aspects of migration, when and why to use it Identify the elements to consider when selecting a technology for your architecture Automate security controls and configuration management Use infrastructure as code and CICD pipelines to run environments in a sustainable manner Understand the management and monitoring capabilities for AWS cloud native application architectures Who this book is for Cloud Native Architectures is for software architects who are keen on designing resilient, scalable, and highly available applications that are native to the cloud.
"Mastering Cloud Native: A Comprehensive Guide to Containers, DevOps, CI/CD, and Microservices" is your essential companion for navigating the transformative world of Cloud Native computing. Designed for both beginners and experienced professionals, this comprehensive guide provides a deep dive into the core principles and practices that define modern software development and deployment. In an era where agility, scalability, and resilience are paramount, Cloud Native computing stands at the forefront of technological innovation. This book explores the revolutionary concepts that drive Cloud Native, offering practical insights and detailed explanations to help you master this dynamic field. The journey begins with an "Introduction to Cloud Native," where you'll trace the evolution of cloud computing and understand the myriad benefits of adopting a Cloud Native architecture. This foundational knowledge sets the stage for deeper explorations into the key components of Cloud Native environments. Containers, the building blocks of Cloud Native applications, are covered extensively in "Understanding Containers." You'll learn about Docker and Kubernetes, the leading technologies in containerization, and discover best practices for managing and securing your containerized applications. The "DevOps in the Cloud Native World" chapter delves into the cultural and technical aspects of DevOps, emphasizing collaboration, automation, and continuous improvement. You'll gain insights into essential DevOps practices and tools, illustrated through real-world case studies of successful implementations. Continuous Integration and Continuous Deployment (CI/CD) are crucial for rapid and reliable software delivery. In the "CI/CD" chapter, you'll explore the principles and setup of CI/CD pipelines, popular tools, and solutions to common challenges. This knowledge will empower you to streamline your development processes and enhance your deployment efficiency. Microservices architecture, a key aspect of Cloud Native, is thoroughly examined in "Microservices Architecture." This chapter highlights the design principles and advantages of microservices over traditional monolithic systems, providing best practices for implementing and managing microservices in your projects. The book also introduces you to the diverse "Cloud Native Tools and Platforms," including insights into the Cloud Native Computing Foundation (CNCF) and guidance on selecting the right tools for your needs. This chapter ensures you have the necessary resources to build and manage robust Cloud Native applications. Security is paramount in any technology stack, and "Security in Cloud Native Environments" addresses the critical aspects of securing your Cloud Native infrastructure. From securing containers and microservices to ensuring compliance with industry standards, this chapter equips you with the knowledge to protect your applications and data. "Monitoring and Observability" explores the importance of maintaining the health and performance of your Cloud Native applications. You'll learn about essential tools and techniques for effective monitoring and observability, enabling proactive identification and resolution of issues. The book concludes with "Case Studies and Real-World Applications," presenting insights and lessons learned from industry implementations of Cloud Native technologies. These real-world examples provide valuable perspectives on the challenges and successes of adopting Cloud Native practices. "Mastering Cloud Native" is more than a technical guide; it's a comprehensive resource designed to inspire and educate. Whether you're a developer, operations professional, or technology leader, this book will equip you with the tools and knowledge to succeed in the Cloud Native era. Embrace the future of software development and unlock the full potential of Cloud Native computing with this indispensable guide.
Get familiar with the principles and techniques for designing cost-effective and scalable cloud-native apps with microservices KEY FEATURES ● Gain a comprehensive understanding of the key concepts and strategies involved in building successful cloud-native microservices applications. ● Discover the practical techniques and methodologies for implementing cloud-native microservices. ● Get insights and best practices for implementing cloud-native microservices. DESCRIPTION Microservices-based cloud-native applications are software applications that combine the architectural principles of microservices with the advantages of cloud-native infrastructure and services. If you want to build scalable, resilient, and agile software solutions that can adapt to the dynamic needs of the modern digital landscape, then this book is for you. This comprehensive guide explores the world of cloud-native microservices and their impact on modern application design. The book covers fundamental principles, adoption frameworks, design patterns, and communication strategies specific to microservices. It then emphasizes on the benefits of scalability, fault tolerance, and resource utilization. Furthermore, the book also addresses event-driven data management, serverless approaches, and security by design. All in all, this book is an essential resource that will help you to leverage the power of microservices in your cloud-native applications. By the end of the book, you will gain valuable insights into building scalable, resilient, and future-proof applications in the era of digital transformation. WHAT YOU WILL LEARN ● Gain insight into the fundamental principles and frameworks that form the foundation of modern application design. ● Explore a comprehensive collection of design patterns tailored specifically for microservices architecture. ● Discover a variety of strategies and patterns to effectively facilitate communication between microservices, ensuring efficient collaboration within the system. ● Learn about event-driven data management techniques that enable real-time processing and efficient handling of data in a distributed microservices environment. ● Understand the significance of security-by-design principles and acquire strategies for ensuring the security of microservices architectures. WHO THIS BOOK IS FOR This book is suitable for cloud architects, developers, and practitioners who are interested in learning about design patterns and strategies for building, testing, and deploying cloud-native microservices. It is also valuable for techno-functional roles, solution experts, pre-sales professionals, and anyone else seeking practical knowledge of cloud-native microservices. TABLE OF CONTENTS 1. Cloud-Native Microservices 2. Modern Application Design Principles 3. Microservice Adoption Framework 4. Design Patterns for Microservices 5. Cloud-Powered Microservices 6. Monolith to Microservices Case Study 7. Inter-Service Communication 8. Event-Driven Data Management 9. The Serverless Approach 10. Cloud Microservices - Security by Design 11. Cloud Migration Strategy
Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. - Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing - Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers - Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
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