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Author: Ross Brigoli
Publisher: Packt Publishing Ltd
ISBN: 1805125850
Category : Computers
Languages : en
Pages : 238
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Book Description
Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you’re a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.
Author: Ross Brigoli
Publisher: Packt Publishing Ltd
ISBN: 1805125850
Category : Computers
Languages : en
Pages : 238
Get Book Here
Book Description
Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you’re a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.
Author: Raman Jhajj
Publisher: BPB Publications
ISBN: 9355519494
Category : Computers
Languages : en
Pages : 284
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Book Description
Harness the power of MLOps for managing real time machine learning project cycle KEY FEATURES ● Comprehensive coverage of MLOps concepts, architecture, tools and techniques. ● Practical focus on building end-to-end ML Systems for Continual Learning with MLOps. ● Actionable insights on CI/CD, monitoring, continual model training and automated retraining. DESCRIPTION MLOps, a combination of DevOps, data engineering, and machine learning, is crucial for delivering high-quality machine learning results due to the dynamic nature of machine learning data. This book delves into MLOps, covering its core concepts, components, and architecture, demonstrating how MLOps fosters robust and continuously improving machine learning systems. By covering the end-to-end machine learning pipeline from data to deployment, the book helps readers implement MLOps workflows. It discusses techniques like feature engineering, model development, A/B testing, and canary deployments. The book equips readers with knowledge of MLOps tools and infrastructure for tasks like model tracking, model governance, metadata management, and pipeline orchestration. Monitoring and maintenance processes to detect model degradation are covered in depth. Readers can gain skills to build efficient CI/CD pipelines, deploy models faster, and make their ML systems more reliable, robust and production-ready. Overall, the book is an indispensable guide to MLOps and its applications for delivering business value through continuous machine learning and AI. WHAT YOU WILL LEARN ● Architect robust MLOps infrastructure with components like feature stores. ● Leverage MLOps tools like model registries, metadata stores, pipelines. ● Build CI/CD workflows to deploy models faster and continually. ● Monitor and maintain models in production to detect degradation. ● Create automated workflows for retraining and updating models in production. WHO THIS BOOK IS FOR Machine learning specialists, data scientists, DevOps professionals, software development teams, and all those who want to adopt the DevOps approach in their agile machine learning experiments and applications. Prior knowledge of machine learning and Python programming is desired. TABLE OF CONTENTS 1. Getting Started with MLOps 2. MLOps Architecture and Components 3. MLOps Infrastructure and Tools 4. What are Machine Learning Systems? 5. Data Preparation and Model Development 6. Model Deployment and Serving 7. Continuous Delivery of Machine Learning Models 8. Continual Learning 9. Continuous Monitoring, Logging, and Maintenance
Author: Siva Guruvareddiar
Publisher: BPB Publications
ISBN: 9365899885
Category : Computers
Languages : en
Pages : 550
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Book Description
DESCRIPTION “Mastering AWS Elastic Kubernetes Services" is your comprehensive guide to understanding and implementing AWS EKS. This book helps you master Kubernetes, the industry-standard container orchestration platform, on the robust and scalable Amazon Web Services (AWS) cloud. This book is a complete guide to Kubernetes and AWS EKS, starting with the basics of Kubernetes architecture and container orchestration. It introduces AWS EKS, explaining its setup, configuration, and fully managed features on AWS. Advanced topics like networking, security, storage, scaling, and AWS EKS cluster optimization are covered in detail. With practical exercises and real-world applications, the book equips readers to confidently deploy, manage, and fine-tune Kubernetes applications on AWS, helping you gain expertise in implementing CI/CD pipelines for AWS EKS deployments, establishing robust networking policies, and architecting storage solutions for various workload requirements. By the end of this book, you will be equipped with the knowledge to design and manage production-ready AWS EKS environments that align with industry best practices and AWS Well-Architected Framework principles. KEY FEATURES ● Learn end-to-end EKS, from core Kubernetes concepts to advanced cluster operations. ● Master practical skills in EKS security, monitoring, and disaster recovery planning. ● Gain expertise in CI/CD, GitOps, and integration with other AWS cloud services. WHAT YOU WILL LEARN ● Design and deploy production-ready EKS clusters from ground up. ● Implement robust security measures and access controls for EKS workloads. ● Build automated CI/CD pipelines and GitOps workflows for EKS deployments. ● Master EKS networking, storage solutions, and AWS service integrations. ● Establish effective monitoring, logging, and troubleshooting strategies for clusters. ● Architect scalable and resilient applications using EKS best practices. WHO THIS BOOK IS FOR Whether you are a DevOps engineer, cloud architect, or platform administrator, you will learn to design, deploy, and maintain production-grade AWS EKS clusters with confidence with the help of this book. TABLE OF CONTENTS 1. Introduction to Kubernetes 2. Kubernetes Architecture 3. Kubernetes Components 4. Introduction to Amazon Elastic Kubernetes Service 5. Amazon Elastic Kubernetes Service Architecture 6. Setting up Elastic Kubernetes Service Prerequisites 7. Creating Elastic Kubernetes Service Clusters 8. Accessing and Configuring Elastic Kubernetes Service Clusters 9. Deploying Sample Apps on AWS Elastic Kubernetes Service Clusters 10. Managing Stateful Apps on AWS Elastic Kubernetes Service Clusters 11. Scaling AWS Elastic Kubernetes Services Workloads 12. Networking with AWS Elastic Kubernetes Services 13. Securing AWS Elastic Kubernetes Service Clusters 14. Storage Options for AWS EKS Workloads 15. Monitoring AWS EKS Clusters 16. Logging and Troubleshooting AWS EKS Clusters 17. Integrating EKS with Other AWS Services 18. Continuous Integration and Continuous Deployment Pipelines for Amazon EKS 19. GitOps Workflows with Amazon EKS 20. Backup and Disaster Recovery with Amazon EKS 21. Amazon EKS Optimization and Best Practices 22. Data Workloads on Amazon EKS 23. Generative Artificial Intelligence on Amazon EKS
Author: Oluwole Fagbohun
Publisher: Packt Publishing Ltd
ISBN: 180324920X
Category : Computers
Languages : en
Pages : 350
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Book Description
Achieve TensorFlow certification with this comprehensive guide covering all exam topics using a hands-on, step-by-step approach—perfect for aspiring TensorFlow developers Key Features Build real-world computer vision, natural language, and time series applications Learn how to overcome issues such as overfitting with techniques such as data augmentation Master transfer learning—what it is and how to build applications with pre-trained models Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional. What you will learn Prepare for success in the TensorFlow Developer Certification exam Master regression and classification modelling with TensorFlow 2.x Build, train, evaluate, and fine-tune deep learning models Combat overfitting using techniques such as dropout and data augmentation Classify images, encompassing preprocessing and image data augmentation Apply TensorFlow for NLP tasks like text classification and generation Predict time series data, such as stock prices Explore real-world case studies and engage in hands-on exercises Who this book is forThis book is for machine learning and data science enthusiasts, as well as data professionals aiming to demonstrate their expertise in building deep learning applications with TensorFlow. Through a comprehensive hands-on approach, this book covers all the essential exam prerequisites to equip you with the skills needed to excel as a TensorFlow developer and advance your career in machine learning. A fundamental grasp of Python programming is the only prerequisite.
Author: Neha Sharma
Publisher: Springer Nature
ISBN: 9811956898
Category : Technology & Engineering
Languages : en
Pages : 211
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Book Description
This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc.
Author: Mansura Habiba
Publisher: Packt Publishing Ltd
ISBN: 1803233672
Category : Computers
Languages : en
Pages : 344
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Book Description
Modernize and migrate smoothly to hybrid cloud infrastructure and successfully mitigate complexities relating to the infrastructure, platform, and production environment Key FeaturesPresents problems and solutions for application modernization based on real-life use casesHelps design and implement efficient, highly available, and scalable cloud-native applicationsTeaches you how to adopt a cloud-native culture for successful deployments on hybrid cloud platformsBook Description Most organizations are now either moving to the cloud through modernization or building their apps in the cloud. Hybrid cloud is one of the best approaches for cloud migration and the modernization journey for any enterprise. This is why, along with coding skills, developers need to know the big picture of cloud footprint and be aware of the integration models between apps in a hybrid and multi-cloud infrastructure. This book represents an overview of your end-to-end journey to the cloud. To be future agnostic, the journey starts with a hybrid cloud. You'll gain an overall understanding of how to approach migration to the cloud using hybrid cloud technologies from IBM and Red Hat. Next, you'll be able to explore the challenges, requirements (both functional and non-functional), and the process of app modernization for enterprises by analyzing various use cases. The book then provides you with insights into the different reference solutions for app modernization on the cloud, which will help you to learn how to design and implement patterns and best practices in your job. By the end of this book, you'll be able to successfully modernize applications and cloud infrastructure in hyperscaler public clouds such as IBM and hybrid clouds using Red Hat technologies as well as develop secure applications for cloud environments. What you will learnStrategize application modernization, from the planning to the implementation phaseApply cloud-native development concepts, methods, and best practicesSelect the right strategy for cloud adoption and modernizationExplore container platforms, storage, network, security, and operationsManage cloud operations using SREs, FinOps, and MLOps principlesDesign a modern data insight hub on the cloudWho this book is for This book is for cloud-native application developers involved in modernizing legacy applications by refactoring and rebuilding them. Cloud solution architects and technical leaders will also find this book useful. It will be helpful to have a basic understanding of cloud-native application development and cloud providers before getting started with this book.
Author: Faisal Masood
Publisher: Packt Publishing Ltd
ISBN: 1803231653
Category : Computers
Languages : en
Pages : 385
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Book Description
Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologies Key Features Build a complete machine learning platform on Kubernetes Improve the agility and velocity of your team by adopting the self-service capabilities of the platform Reduce time-to-market by automating data pipelines and model training and deployment Book Description MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built. What you will learn Understand the different stages of a machine learning project Use open source software to build a machine learning platform on Kubernetes Implement a complete ML project using the machine learning platform presented in this book Improve on your organization's collaborative journey toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.
Author: Tarun Vashishth
Publisher: BPB Publications
ISBN: 9355517181
Category : Computers
Languages : en
Pages : 284
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Book Description
Empowering you to investigate, analyze, and secure the digital realm KEY FEATURES ● Comprehensive coverage of all digital forensics concepts. ● Real-world case studies and examples to illustrate techniques. ● Step-by-step instructions for setting up and using essential forensic tools. ● In-depth exploration of volatile and non-volatile data analysis. DESCRIPTION Digital forensics is the art and science of extracting the hidden truth and this book is your hands-on companion, bringing the world of digital forensics to life. Starting with the core principles of digital forensics, the book explores the significance of various case types, the interconnectedness of the field with cybersecurity, and the ever-expanding digital world's challenges. As you progress, you will explore data acquisition, image formats, digital evidence preservation, file carving, metadata extraction, and the practical use of essential forensic tools like HxD, The Sleuth Kit, Autopsy, Volatility, and PowerForensics. The book offers step-by-step instructions, real-world case studies, and practical examples, ensuring that beginners can confidently set up and use forensic tools. Experienced professionals, on the other hand, will find advanced insights into memory analysis, network forensics, anti-forensic techniques, and more. This book empowers you to become a digital detective, capable of uncovering data secrets, investigating networks, exploring volatile and non-volatile evidence, and understanding the intricacies of modern browsers and emails. WHAT YOU WILL LEARN ● Learn how to set up and use digital forensic tools, including virtual environments. ● Learn about live forensics, incident response, and timeline examination. ● In-depth exploration of Windows Registry and USBs. ● Network forensics, PCAPs, and malware scenarios. ● Memory forensics, malware detection, and file carving. ● Advance tools like PowerForensics and Autopsy. WHO THIS BOOK IS FOR Whether you are a tech-savvy detective, a curious student, or a seasoned cybersecurity pro seeking to amplify your skillset. Network admins, law enforcement officers, incident responders, aspiring analysts, and even legal professionals will find invaluable tools and techniques within these pages. TABLE OF CONTENTS 1. Introduction to Essential Concepts of Digital Forensics 2. Digital Forensics Lab Setup 3. Data Collection: Volatile and Non-Volatile 4. Forensics Analysis: Live Response 5. File System and Log Analysis 6. Windows Registry and Artifacts 7. Network Data Collection and Analysis 8. Memory Forensics: Techniques and Tools 9. Browser and Email Forensics 10. Advanced Forensics Tools, Commands and Methods 11. Anti-Digital Forensics Techniques and Methods
Author: Xavier Vasques
Publisher: John Wiley & Sons
ISBN: 1394220618
Category : Computers
Languages : en
Pages : 516
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Book Description
Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
Author: Yuan Tang
Publisher: Simon and Schuster
ISBN: 1638354197
Category : Computers
Languages : en
Pages : 375
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Book Description
Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation
