Cloud Based Machine Learning Practical Guide To Deploying Ai Models In The Cloud

Download or read book Cloud Based Machine Learning – Practical Guide to Deploying AI Models in the Cloud written by Hemanth Volikatla and published by RK Publication. This book was released on 2024-05-15 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Building Machine Learning and Deep Learning Models on Google Cloud Platform written by Ekaba Bisong and published by Apress. This book was released on 2019-09-27 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Pragmatic AI written by Noah Gift and published by Addison-Wesley Professional. This book was released on 2018-07-12 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Practical Deep Learning for Cloud, Mobile, and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Enterprise AI in the Cloud written by Rabi Jay and published by John Wiley & Sons. This book was released on 2023-12-20 with total page 763 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Cloud Native Development with Azure written by Pavan Verma and published by BPB Publications. This book was released on 2024-03-19 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Introducing MLOps written by Mark Treveil and published by "O'Reilly Media, Inc.". This book was released on 2020-11-30 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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