Google Cloud Platform For Enterprise Mlops A Practical Guide To Cloud Computing Part One
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Google Cloud Platform For Enterprise Mlops A Practical Guide To Cloud Computing Part One
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Author : Jothi Periasamy
language : en
Publisher: Jothi Periasamy
Release Date : 2022-12-12
Google Cloud Platform For Enterprise Mlops A Practical Guide To Cloud Computing Part One written by Jothi Periasamy and has been published by Jothi Periasamy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-12 with Computers categories.
Google Cloud Platform For Enterprise Mlops A Practical Guide To Cloud Computing Part One
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Author : Jothi Periasamy
language : en
Publisher: Jothi Periasamy
Release Date : 2022-12-12
Google Cloud Platform For Enterprise Mlops A Practical Guide To Cloud Computing Part One written by Jothi Periasamy and has been published by Jothi Periasamy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-12 with Computers categories.
Data Engineering With Google Cloud Platform
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Author : Adi Wijaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-31
Data Engineering With Google Cloud Platform written by Adi Wijaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with Computers categories.
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
Enterprise Ai In The Cloud
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Author : Rabi Jay
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-20
Enterprise Ai In The Cloud written by Rabi Jay and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-20 with Computers categories.
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.
A Guide To Implementing Mlops
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Author : Prafful Mishra
language : en
Publisher: Springer Nature
Release Date : 2025-02-01
A Guide To Implementing Mlops written by Prafful Mishra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-01 with Computers categories.
Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.
Google Cloud Digital Leader Certification Guide
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Author : Bruno Beraldo Rodrigues
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-15
Google Cloud Digital Leader Certification Guide written by Bruno Beraldo Rodrigues and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-15 with Computers categories.
Gain the expertise needed for the Google Cloud Digital Leader certification with the help of industry insights, effective testing strategies, and exam questions designed to help you make informed tech decisions aligned with business goals Key Features Learn about data management, AI, monetization, security, and the significance of infrastructure modernization Build a solid foundation in Google Cloud, covering all technical essentials necessary for a Google Cloud Digital Leader Test your knowledge of cloud and digital transformation through realistic exam questions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTo thrive in today's world, leaders and technologists must understand how technology shapes businesses. As organizations shift from self-hosted to cloud-native solutions, embracing serverless systems, strategizing data use, and defining monetization becomes imperative. The Google Cloud Digital Leader Certification Guide lays a solid foundation of industry knowledge, focused on the Google Cloud platform and the innovative ways in which customers leverage its technologies. The book starts by helping you grasp the essence of digital transformation within the Google Cloud context. You’ll then cover core components of the platform, such as infrastructure and application modernization, data innovation, and best practices for environment management and security. With a series of practice exam questions included, this book ensures that you build comprehensive knowledge and prepare to certify as a Google Cloud Digital Leader. Going beyond the exam essentials, you’ll also explore how companies are modernizing infrastructure, data ecosystems, and teams in order to capitalize on new market opportunities through platform expertise, best practices, and real-world scenarios. By the end of this book, you'll have learned everything you need to pass the Google Cloud Digital Leader certification exam and have a reference guide for future requirements.What you will learn Leverage Google Cloud’s AI and ML solutions to create business value Identify Google Cloud solutions for data management and smart analytics Acquire the skills necessary to modernize infrastructure and applications on GCP Understand the value of APIs and their applications in cloud environments Master financial governance and implement best practices for cost management Understand the cloud security approach and benefits of Google Cloud security Find out how IT operations must adapt to thrive in the cloud Who this book is for This Google Cloud fundamentals book is suitable for individuals with both technical and non-technical backgrounds looking for a starting point to pursue more advanced Google Cloud certifications. No prior experience is required to get started with this book; only a keen interest in learning and exploring cloud concepts, with a focus on Google Cloud.
Ace The Google Machine Learning Engineer Certification
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Author : Etienne Noumen
language : en
Publisher: Etienne Noumen
Release Date :
Ace The Google Machine Learning Engineer Certification written by Etienne Noumen and has been published by Etienne Noumen this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Master Google Cloud’s most advanced AI certification with this definitive 2025 study guide. From TensorFlow and data pipelines to ML ops, model deployment, and ethical AI—this book delivers the knowledge, tools, and confidence to help you ace the Professional Machine Learning Engineer Exam. Backed by real-world examples, mock exams, and hands-on insights. 🎯 The ins and outs of Google's Machine Learning Engineer certification are explored in detail. A comprehensive guide is provided, covering the latest updates and offering tips for success. Why This Certification Matters - The growing demand for skilled Machine Learning Engineers - Career advancement and increased earning potential - The Google brand and its weight in the tech world Decoding the Certification: Requirements & Exam Structure - The four main exam domains: Machine Learning Concepts, Data Analysis, Model Building and Evaluation, and Machine Learning Systems Design - Exam format and structure: Multiple-choice, coding, and open-ended questions - The Google Cloud Platform (GCP) proficiency requiredMastering the Material: Essential Skills & Resources - Key concepts: Supervised and unsupervised learning, deep learning, natural language processing, computer vision - Recommended resources: Coursera, Udacity, Google Cloud Skills Boost, and relevant online communities - Practical projects: Building your own portfolio to showcase your skills Strategies for Success: Effective Preparation & Exam Day Tips - Practice, practice, practice: Using mock exams, coding exercises, and real-world datasets - Time management: Balancing learning, practice, and exam-day strategy - Stress management: Techniques to stay calm and focused on exam day Full Practice Exam - 2025 included Beyond the Certification: Career Paths & Continued Learning - The book explores potential roles: Machine Learning Engineer, Data Scientist, AI Researcher - The importance of continuous learning and staying updated with advancements in the field - Building your professional network and actively contributing to the ML community 📘 Download the E-Book + Audiobook combo at Djamgatech at https://djamgatech.com/product/ace-the-google-machine-learning-engineer-certification-2025-update-e-book-audiobook/ 📘 You can also Download the E-Book + Audiobook combo at Google Play Books at https://play.google.com/store/audiobooks/details?id=AQAAAEDKqGjosM
Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation
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Author : Phanish Lakkarasu
language : en
Publisher: Deep Science Publishing
Release Date : 2025-06-06
Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation written by Phanish Lakkarasu and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
In today’s fast-paced digital era, organizations are under constant pressure to innovate, scale, and deliver intelligent services with speed and reliability. Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI-Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation is a comprehensive exploration into the foundational and advanced components required to build robust, future-ready cloud ecosystems. This book is the product of years of observing the shifting paradigms in enterprise IT—from legacy systems and monolithic architectures to microservices, serverless computing, and AI-powered infrastructure. At the heart of this evolution lies the need for cloud-native platforms that are not only scalable and resilient but also intelligent and automation-ready. The content in these pages is aimed at architects, engineers, data scientists, DevOps professionals, and digital transformation leaders who seek to understand and implement the key building blocks of modern cloud systems. It delves into the design principles behind scalable infrastructure, best practices for integrating AI and Machine Learning, and the implementation of MLOps pipelines to streamline deployment, monitoring, and continuous improvement of ML models. Furthermore, it provides practical insights into data engineering strategies that ensure secure, efficient, and real-time data flow across distributed environments. We also explore critical topics such as multi-cloud and hybrid cloud strategies, edge computing, observability, cost optimization, and governance—ensuring that readers are equipped to tackle both the technical and operational challenges of building next-generation platforms. What sets this book apart is its unified approach to cloud, AI, and data engineering—treating them not as isolated silos but as interconnected pillars of intelligent digital transformation. Whether you are designing enterprise-grade solutions or modernizing existing infrastructures, this guide will serve as your companion in navigating complexity with clarity and confidence.
Practical Guide To H2o Ai
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-31
Practical Guide To H2o Ai written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-31 with Computers categories.
"Practical Guide to H2O.ai" "Practical Guide to H2O.ai" is a comprehensive resource designed for data scientists, machine learning engineers, and IT professionals who seek to master the full capabilities of H2O.ai’s powerful platform. This guide delivers a deep dive into the architecture and components of the H2O ecosystem—including H2O-3 and Driverless AI—while demystifying its integration within diverse enterprise environments, whether on-premises, cloud, or hybrid. Readers will gain actionable insights into secure system deployment, cluster management, large-scale data ingestion, and optimized ETL workflows, ensuring robust infrastructure that meets the demands of modern data-driven organizations. Structured to support both practical adoption and technical excellence, the book traverses core machine learning tasks, from advanced preprocessing and feature engineering to supervised and unsupervised learning with leading algorithms such as GBM, XGBoost, and deep neural networks. Special emphasis is placed on scalable automation through H2O AutoML, presenting real-world case studies while showcasing best practices in algorithm selection, hyperparameter optimization, and model evaluation. Dedicated chapters explore explainable AI and responsible ML practices—covering interpretability, bias mitigation, compliance, and data privacy—empowering readers to build transparent, auditable, and trustworthy solutions for complex, regulated domains. With detailed coverage of emerging fields like natural language processing, time series analysis, MLOps, and distributed deep learning, "Practical Guide to H2O.ai" is an indispensable reference for leveraging H2O.ai at scale. Topics such as advanced model deployment, real-time inference, CI/CD integration, and production troubleshooting combine theory with hands-on strategies for operationalizing machine learning workflows. Whether you are scaling to petabyte data, orchestrating containerized clusters, or exploring cutting-edge areas like federated learning and edge ML, this guide equips you with the knowledge and tools to drive innovation and achieve enterprise-level AI success.
Machine Learning At Scale With H2o
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Author : Gregory Keys
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-07-29
Machine Learning At Scale With H2o written by Gregory Keys and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with Computers categories.
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.