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Author: Hemanth Volikatla
Publisher: RK Publication
ISBN: 8197364567
Category : Computers
Languages : en
Pages : 301
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Book Description
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.
Author: Hemanth Volikatla
Publisher: RK Publication
ISBN: 8197364567
Category : Computers
Languages : en
Pages : 301
Get Book Here
Book Description
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.
Author: Ekaba Bisong
Publisher: Apress
ISBN: 1484244702
Category : Computers
Languages : en
Pages : 703
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Book Description
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
Author: Noah Gift
Publisher: Addison-Wesley Professional
ISBN: 0134863917
Category : Computers
Languages : en
Pages : 720
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Book Description
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.
Author: Rabi Jay
Publisher: John Wiley & Sons
ISBN: 1394213069
Category : Computers
Languages : en
Pages : 763
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Book Description
Embrace emerging AI trends and integrate your operations with cutting-edge solutions Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You'll also discover best practices on optimizing cloud infrastructure for scalability and automation. Enterprise AI in the Cloud helps you gain a solid understanding of: AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments. With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.
Author: Anirudh Koul
Publisher: "O'Reilly Media, Inc."
ISBN: 1492034819
Category : Computers
Languages : en
Pages : 585
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Book Description
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Author: Pavan Verma
Publisher: BPB Publications
ISBN: 9355517718
Category : Computers
Languages : en
Pages : 300
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Book Description
Develop cloud-native skills by learning Azure cloud infrastructure offerings KEY FEATURES ● Master cloud-native development fundamentals and Azure services. ● Application security, monitoring, and efficient management. ● Explore advanced services like Azure Machine Learning & IoT Hub. DESCRIPTION Azure is a powerful cloud computing platform with a wide range of services. Reading this book can help you gain an in-depth understanding of these services and how to use them effectively. Being one of the most popular cloud computing platforms, having knowledge and skills in Azure can be a valuable asset in your career. Explore Microsoft Azure for cloud-native development. Understand its basics, benefits, and services. Learn about identity management, compute resources, and application building. Discover containerization with Azure Kubernetes Service and Azure Container Registry. Dive into microservices architecture and serverless development with Azure Functions. Understand security, monitoring, logging, and CI/CD pipelines with Azure DevOps. Finally, explore advanced services like Azure Machine Learning and Azure IoT Hub, with real-world case studies and insights into future trends. Azure is constantly evolving, with new features and services being added regularly. Reading books on Azure cloud can help you stay up-to-date with the latest developments in the platform and keep your skills current. WHAT YOU WILL LEARN ● Design and build scalable cloud-native apps. ● Utilize Azure services for identity, compute, and storage. ● Implement containerization for efficient packaging and deployment. ● Secure applications with robust Azure security features. ● Manage and monitor applications for optimal performance and reliability. WHO THIS BOOK IS FOR This book is ideal for software developers, architects, and cloud engineers looking to build and deploy modern, scalable applications on the Microsoft Azure cloud platform. TABLE OF CONTENTS 1. Introduction to cloud and cloud native development 2. Azure Services for Cloud Native Development 3. Data Storage Services on Azure Cloud 4. Azure Kubernetes and Container Registry 5. Developing Applications on Azure 6. Monitoring And Logging Applications on Azure 7. Security and Governance on Azure 8. Deploying Applications on Azure 9. Advance Azure Services 10. Case Studies and best practice 11. Generative AI and Future Trends
Author: Pedram Ariel Rostami
Publisher: Starseed AI
ISBN:
Category : Education
Languages : en
Pages : 291
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Book Description
"A Practical Guide to Machine Learning and AI: Part-I" is an essential resource for anyone looking to dive into the world of artificial intelligence and machine learning. Whether you're a complete beginner or have some experience in the field, this book will equip you with the fundamental knowledge and hands-on skills needed to harness the power of these transformative technologies. In this comprehensive guide, you'll embark on an engaging journey that starts with the basics of data engineering. You'll gain a solid understanding of big data, the key roles involved, and how to leverage the versatile Python programming language for data-centric tasks. From mastering Python data types and control structures to exploring powerful libraries like NumPy and Pandas, you'll build a strong foundation to tackle more advanced concepts. As you progress, the book delves into the realm of exploratory data analysis (EDA), where you'll learn techniques to clean, transform, and extract insights from your data. This sets the stage for the heart of the book - machine learning. You'll explore both supervised and unsupervised learning, diving deep into regression, classification, clustering, and dimensionality reduction algorithms. Along the way, you'll encounter real-world examples and hands-on exercises to reinforce your understanding and apply what you've learned. But this book goes beyond just the technical aspects. It also addresses the ethical considerations surrounding machine learning, ensuring you develop a well-rounded perspective on the responsible use of these powerful tools. Whether your goal is to jumpstart a career in data science, enhance your existing skills, or simply satisfy your curiosity about the latest advancements in AI, "A Practical Guide to Machine Learning and AI: Part-I" is your comprehensive companion. Prepare to embark on an enriching journey that will equip you with the knowledge and skills to navigate the exciting frontiers of artificial intelligence and machine learning.
Author: Gregory Keys
Publisher: Packt Publishing Ltd
ISBN: 1800569297
Category : Computers
Languages : en
Pages : 396
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Book Description
Build predictive models using large data volumes and deploy them to production using cutting-edge techniques Key Features • Build highly accurate state-of-the-art machine learning models against large-scale data • Deploy models for batch, real-time, and streaming data in a wide variety of target production systems • Explore all the new features of the H2O AI Cloud end-to-end machine learning platform Book Description H2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments. Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You'll start by exploring H2O's in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You'll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You'll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you'll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities. By the end of this book, you'll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs. What you will learn • Build and deploy machine learning models using H2O • Explore advanced model-building techniques • Integrate Spark and H2O code using H2O Sparkling Water • Launch self-service model building environments • Deploy H2O models in a variety of target systems and scoring contexts • Expand your machine learning capabilities on the H2O AI Cloud Who this book is for This book is for data scientists and machine learning engineers who want to gain hands-on machine learning experience by building and deploying state-of-the-art models with advanced techniques using H2O technology. An understanding of the data science process and experience in Python programming is recommended. This book will also benefit students by helping them understand how machine learning works in real-world enterprise scenarios.
Author: Mark Treveil
Publisher: "O'Reilly Media, Inc."
ISBN: 1098116429
Category : Computers
Languages : en
Pages : 163
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Book Description
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
Author: Ronald Bruinsma
Publisher: Ronald Bruinsma
ISBN: 9083342212
Category : Computers
Languages : en
Pages : 215
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Book Description
Highlights Packed with useful advice and practical insights to help you bypass typical obstacles and get started efficiently with implementing an Azure Cloud environment. Offers extensive understanding on all Azure cloud-related aspects, from the initial stages to ongoing management, making your journey smoother. Discusses a wide range of topics, from creating an effective strategy to long-term Azure cloud governance. Book Description This book is an in-depth guide on cloud adoption, specifically focusing on the Microsoft Azure platform. It presents a step-by-step approach for businesses looking to commence on their digital transformation journey by leveraging Azure's capabilities. Designed to help organizations understand and apply the Cloud Adoption Framework (CAF), it discusses the strategic aspects of cloud adoption, from business case formulation to planning and execution. The book kicks off with a detailed overview of the CAF, its key components, and how it aligns with your organization's business strategy. Then, it navigates through the various stages of the CAF process, including the Strategy, Plan, Ready, and Adopt phases, providing essential insights into the complexities involved in each step. It further delves into technical aspects, discussing the configuration of Azure environments, cloud operations management, and the critical role of security and compliance in a cloud-based infrastructure. This guide also highlights cost management strategies, showcasing how Azure's flexible pricing models can lead to significant savings over time. It demonstrates the power of automation in managing cloud operations and the potential benefits of Infrastructure as Code (IaC) methodologies. What sets this book apart is its focus on practical implementation, filled with real-world examples, best practices, and common pitfalls to avoid. The approach is both comprehensive and modular, catering to readers new to Azure as well as those with experience in the cloud domain. By the end of this guide, you'll have a clear understanding of how to implement and manage an Azure environment that aligns with your organization's needs, thus facilitating a successful cloud migration and ongoing digital transformation. Whether you're a business leader, IT professional, or simply an enthusiast looking to understand the complexities of cloud adoption, this book serves as a reliable resource, providing a solid foundation in Azure cloud adoption as per the CAF guidelines. Table of Contents Introduction to Cloud Adoption Framework (CAF): This chapter introduces the readers to the concept of the Cloud Adoption Framework, its importance, and the various stages involved in the process. Strategize and Plan: It guides you through the process of establishing key performance indicators (KPIs), assessing your digital estate, and formulating a cloud adoption plan. Ready Phase: Here, we discuss the readiness aspect of cloud adoption. This includes preparing the digital environment, capacity planning, and establishing a cloud adoption team. Adopt Phase: It covers topics like infrastructure setup, data migration, application innovation, and provides guidance on managing possible challenges. Govern and Manage: It offers detailed insights on cost management, security and compliance, and how to establish a robust monitoring and incident response system. Secure and Organize Phase: . It includes security considerations, aligning your organization and teams, and understanding the importance of Azure landing zones. Implementing Best Practices: The final chapter shares the 11 best practices for implementing the Cloud Adoption Framework.
