Learn Vertex Ai
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The Definitive Guide To Google Vertex Ai
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Author : Jasmeet Bhatia
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-29
The Definitive Guide To Google Vertex Ai written by Jasmeet Bhatia 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 2023-12-29 with Computers categories.
Implement machine learning pipelines with Google Cloud Vertex AI Key Features Understand the role of an AI platform and MLOps practices in machine learning projects Get acquainted with Google Vertex AI tools and offerings that help accelerate the creation of end-to-end ML solutions Implement Vision, NLP, and recommendation-based real-world ML models on Google Cloud Platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWhile AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management. This book is a comprehensive guide that lets you explore Google Vertex AI’s easy-to-advanced level features for end-to-end ML solution development. Throughout this book, you’ll discover how Vertex AI empowers you by providing essential tools for critical tasks, including data management, model building, large-scale experimentations, metadata logging, model deployments, and monitoring. You’ll learn how to harness the full potential of Vertex AI for developing and deploying no-code, low-code, or fully customized ML solutions. This book takes a hands-on approach to developing u deploying some real-world ML solutions on Google Cloud, leveraging key technologies such as Vision, NLP, generative AI, and recommendation systems. Additionally, this book covers pre-built and turnkey solution offerings as well as guidance on seamlessly integrating them into your ML workflows. By the end of this book, you’ll have the confidence to develop and deploy large-scale production-grade ML solutions using the MLOps tooling and best practices from Google.What you will learn Understand the ML lifecycle, challenges, and importance of MLOps Get started with ML model development quickly using Google Vertex AI Manage datasets, artifacts, and experiments Develop no-code, low-code, and custom AI solution on Google Cloud Implement advanced model optimization techniques and tooling Understand pre-built and turnkey AI solution offerings from Google Build and deploy custom ML models for real-world applications Explore the latest generative AI tools within Vertex AI Who this book is for If you are a machine learning practitioner who wants to learn end-to-end ML solution development on Google Cloud Platform using MLOps best practices and tools offered by Google Vertex AI, this is the book for you.
Learn Vertex Ai
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Author : Diego Rodrigues
language : en
Publisher: Independently Published
Release Date : 2025-06-27
Learn Vertex Ai written by Diego Rodrigues and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Computers categories.
LEARN VERTEX AI Implement Enterprise AI on Google Cloud This book is aimed at technology professionals, data engineers, and students who want to master the use of Vertex AI for creating, automating, and governing artificial intelligence projects in corporate Google Cloud environments. Learn how to structure machine learning pipelines, integrate data, automate deployment and versioning processes, monitor performance, and implement MLOps and DataOps practices with security, scalability, and compliance. Explore practical integrations with BigQuery, Dataflow, Pub/Sub, Cloud Storage, as well as leading frameworks such as TensorFlow, PyTorch, and scikit-learn. Develop skills in multi-cloud deployment, model tuning, cost control, CI/CD automation, and complete governance of the data and model lifecycle. - Professional setup of Vertex AI on Google Cloud - Building automated and scalable machine learning pipelines - Advanced integration with BigQuery, Dataflow, Pub/Sub, and Cloud Storage - Deployment, versioning, and monitoring of production models - Orchestration with TensorFlow, PyTorch, scikit-learn, AutoML, and containers - CI/CD automation, performance tuning, cost control - Implementation of Feature Store, Model Registry, and access policies - Governance, auditing, compliance, and data security in AI - MLOps, DataOps strategies, and multi-cloud integration - Real-world applications, certification preparation, and critical projects Master Vertex AI and become a reference in corporate AI, delivering scalable, auditable projects aligned with global market demands. vertex ai, google cloud, machine learning, nvidia, pipelines, automation, bigquery, dataflow, pub/sub, cloud storage, ci/cd, mlops, automl, tensorflow, pytorch, feature store, model registry, dataops, model deployment, orchestration, monitoring, governance, data security
Vertex Ai
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Author : Ryan E Davis
language : en
Publisher: Independently Published
Release Date : 2025-12-17
Vertex Ai written by Ryan E Davis and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-17 with Computers categories.
Unlock the Full Potential of Machine Learning on Google Cloud with Vertex AI Are you ready to transform your organization's data into actionable insights and drive business growth with machine learning? Look no further! "Vertex AI Building, Training, Deploying, and Scaling Enterprise-Grade Machine Learning on Google Cloud" is your ultimate guide to harnessing the power of Google Cloud's unified AI platform. In this comprehensive book, you'll discover how to: Build and train machine learning models with Vertex AI's intuitive interface and automated workflows Deploy and manage models at scale, with robust serving and monitoring capabilities Accelerate innovation with pre-built algorithms, AutoML, and custom model training Streamline MLOps with integrated tools and best practices for model versioning, testing, and deployment What You'll Learn: Master Vertex AI's core features, including AutoML, custom training, and model serving Develop and deploy machine learning models for image, text, and tabular data Implement MLOps best practices for model management, versioning, and CI/CD Leverage Google Cloud's scalable infrastructure for large-scale machine learning workloads Integrate Vertex AI with other Google Cloud services, such as BigQuery and Dataflow Why Choose This Book? Expert Insights: Gain knowledge from experienced practitioners who have worked with Vertex AI in real-world enterprise settings. Practical Examples: Learn through hands-on exercises and case studies that demonstrate Vertex AI's capabilities. Future-Proof Your Skills: Stay ahead of the curve with the latest advancements in machine learning and Google Cloud technology. Get Started with Vertex AI Today! Take the first step towards unlocking the full potential of machine learning on Google Cloud. Order now and start building, training, deploying, and scaling enterprise-grade machine learning models with Vertex AI.
Vertex Ai Crash Course
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Author : Freddie Becka
language : en
Publisher: Independently Published
Release Date : 2025-12-16
Vertex Ai Crash Course written by Freddie Becka and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-16 with Computers categories.
Vertex AI Crash Course A Fast-Track Guide to Multimodal AI, Gemini Models, Agentic Workflows, and Production-Ready ML on Google Cloud Vertex AI Crash Course is the definitive, hands-on guide for developers, AI engineers, and data scientists who want to move beyond theory and build real, production-ready AI systems on Google Cloud. As artificial intelligence rapidly shifts toward multimodal models, large language models, and agentic systems, Vertex AI has become the central platform for deploying modern machine learning and generative AI at scale. This book cuts through the noise and shows you exactly how to use Vertex AI to design, build, deploy, and operate intelligent systems that work in the real world. Written by seasoned AI engineer Freddie Becka, this crash course focuses on practical execution, modern architectures, and production best practices rather than academic abstractions or toy examples. What You Will Learn In this book, you will learn how to: Build and deploy machine learning models using Vertex AI AutoML and custom training workflows Work with Gemini models for text, vision, and multimodal reasoning Design agentic AI systems that plan, reason, and act using modern orchestration patterns Implement retrieval-augmented generation (RAG) with embeddings and vector search Develop scalable multimodal AI applications that combine text, images, and structured data Create end-to-end ML and GenAI pipelines, from data ingestion to deployment and monitoring Apply MLOps best practices including model versioning, CI/CD, monitoring, and retraining Optimize cost, performance, and reliability for production workloads on Google Cloud Understand architectural trade-offs between AutoML, custom models, and LLM-based solutions Each concept is explained clearly, then reinforced with real-world Python implementations, detailed engineering commentary, and practical guidance drawn from production experience. Who This Book Is For This book is ideal for: Software developers transitioning into AI and machine learning Data scientists and ML engineers building systems on Google Cloud AI engineers working with LLMs, multimodal models, and agentic workflows Cloud architects and technical leads responsible for AI infrastructure Advanced learners who want a fast but serious path to mastery If you are tired of fragmented tutorials and want a single, coherent guide to modern AI development on Vertex AI, this book is for you. Why This Book Is Different Unlike many Vertex AI books that focus only on traditional machine learning, Vertex AI Crash Course reflects the current reality of AI development: It integrates classical ML, generative AI, and agentic systems in one workflow It emphasizes production readiness, not just experimentation It is written for practitioners, not beginners or purely academic readers It covers modern topics like Gemini, multimodal AI, RAG, and agents, which most existing books overlook This is not just a tutorial. It is a field guide for building intelligent systems that scale. Build Faster. Deploy Smarter. Scale with Confidence. Whether you are deploying your first model or designing enterprise-grade AI platforms, Vertex AI Crash Course gives you the clarity, structure, and hands-on knowledge needed to succeed with modern AI on Google Cloud.
Journey To Become A Google Cloud Machine Learning Engineer
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Author : Dr. Logan Song
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-09-20
Journey To Become A Google Cloud Machine Learning Engineer written by Dr. Logan Song 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 2022-09-20 with Computers categories.
Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills Key FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook Description This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is for Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book.
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
Data Science Quick Reference Manual Advanced Machine Learning And Deployment
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date :
Data Science Quick Reference Manual Advanced Machine Learning And Deployment written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.
Gcp Pmle Practice Questions For Google Professional Machine Learning Engineer Certification
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Author : Dormouse Quillsby
language : en
Publisher: Dormouse Quillsby
Release Date :
Gcp Pmle Practice Questions For Google Professional Machine Learning Engineer Certification written by Dormouse Quillsby and has been published by Dormouse Quillsby this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
NotJustExam - GCP-PMLE Practice Questions for Google Professional Machine Learning Engineer Certification Struggling to find quality study materials for the Google Certified Professional Machine Learning Engineer (GCP-PMLE) exam? Our question bank offers over 300+ carefully selected practice questions with detailed explanations, insights from online discussions, and AI-enhanced reasoning to help you master the concepts and ace the certification. Say goodbye to inadequate resources and confusing online answers—we’re here to transform your exam preparation experience! Why Choose Our GCP-PMLE Question Bank? Have you ever felt that official study materials for the GCP-PMLE exam don’t cut it? Ever dived into a question bank only to find too few quality questions? Perhaps you’ve encountered online answers that lack clarity, reasoning, or proper citations? We understand your frustration, and our GCP-PMLE certification prep is designed to change that! Our GCP-PMLE question bank is more than just a brain dump—it’s a comprehensive study companion focused on deep understanding, not rote memorization. With over 300+ expertly curated practice questions, you get: Question Bank Suggested Answers – Learn the rationale behind each correct choice. Summary of Internet Discussions – Gain insights from online conversations that break down complex topics. AI-Recommended Answers with Full Reasoning and Citations – Trust in clear, accurate explanations powered by AI, backed by reliable references. Your Path to Certification Success This isn’t just another study guide; it’s a complete learning tool designed to empower you to grasp the core concepts of Professional Machine Learning Engineer. Our practice questions prepare you for every aspect of the GCP-PMLE exam, ensuring you’re ready to excel. Say goodbye to confusion and hello to a confident, in-depth understanding that will not only get you certified but also help you succeed long after the exam is over. Start your journey to mastering the Google Certified: Professional Machine Learning Engineer certification today with our GCP-PMLE question bank! Learn more: Google Certified: Professional Machine Learning Engineer https://cloud.google.com/learn/certification/machine-learning-engineer
Data Science Manuale Italiano Advanced Machine Learning E Deployment
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date :
Data Science Manuale Italiano Advanced Machine Learning E Deployment written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Questa opera segue il curriculum 2021 della Association for Computing Machinery per specialisti in Scienze dei Dati, con l’obiettivo di costituire un “Bignami” della Scienza ed Ingegneria dei Dati e facilitare il percorso di formazione personale a partire da competenze specialistiche in Informatica o Matematica o Statistica per un lettore di lingua madre italiana. Parte di una serie di testi, riepiloga prima di tutto la metodologia di lavoro standard CRISP DM utilizzata in questa opera e in progetti di Scienza dei Dati. Poichè questo testo utilizza Orange per gli aspetti applicativi, ne descrive l’installazione ed i widget. La fase di modellizzazione dei dati viene considerata nell’ottica dell’apprendimento automatico riepilogando i tipi di apprendimento automatico, i tipi di modelli, i tipi di problemi e i tipi di algoritmi. Sono descritti gli aspetti avanzati associati alla modellizzazione quali le funzioni di perdita e di ottimizzazione come la gradient descent, le tecniche per analizzare le prestazioni dei modelli come il Bootstrapping e la Cross Validation. Vengono analizzati gli scenari di deployment e le più comuni piattaforme, con esempi applicativi. Vengono proposti i meccanismi per automatizzare l’apprendimento automatico e per supportare l’interpretabilità dei modelli e dei risultati come Partial Dependence Plot, Permuted Feature Importance e altre. Gli esercizi sono descritti con Orange e Python con l’uso della libreria Keras/Tensorflow. Il testo è corredato di materiale di supporto ed è possibile scaricare gli esempi in Orange e i dati di prova.
Data Engineering For Machine Learning Pipelines
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Author : Pavan Kumar Narayanan
language : en
Publisher: Springer Nature
Release Date : 2024-09-27
Data Engineering For Machine Learning Pipelines written by Pavan Kumar Narayanan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with Computers categories.
This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is For Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists