| Description |
1 online resource |
| Summary |
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 Description While 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. |
| Contents |
Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: The Importance of MLOps in a Real-World ML Deployment -- Chapter 1: Machine Learning Project Life Cycle and Challenges -- ML project life cycle -- Common challenges in developing real-world ML solutions -- Data collection and security -- Non-representative training data -- Poor quality of data -- Underfitting the training dataset -- Overfitting the training dataset -- Infrastructure requirements -- Limitations of ML -- Data-related concerns -- Deterministic nature of problems |
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Lack of interpretability and reproducibility -- Concerns related to cost and customizations -- Ethical concerns and bias -- Summary -- Chapter 2: What Is MLOps, and Why Is It So Important for Every ML Team? -- Why is MLOps important? -- Implementing different MLOps maturity levels -- MLOps maturity level 0 -- MLOps maturity level 1 -- automating basic ML steps -- MLOps maturity level 2 -- automated model deployments -- How can Vertex AI help with implementing MLOps? -- Summary -- Part 2: Machine Learning Tools for Custom Models on Google Cloud |
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Chapter 3: It's All About Data -- Options to Store and Transform ML Datasets -- Moving data to Google Cloud -- Google Cloud Storage Transfer tools -- BigQuery Data Transfer Service -- Storage Transfer Service -- Transfer Appliance -- Where to store data -- GCS -- object storage -- BQ -- data warehouse -- Transforming data -- Ad hoc transformations within Jupyter Notebook -- Cloud Data Fusion -- Dataflow pipelines for scalable data transformations -- Summary -- Chapter 4: Vertex AI Workbench -- a One-Stop Tool for AI/ML Development Needs -- What is Jupyter Notebook? |
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Getting started with Jupyter Notebook -- Vertex AI Workbench -- Getting started with Vertex AI Workbench -- Custom containers for Vertex AI Workbench -- Scheduling notebooks in Vertex AI -- Configuring notebook executions -- Summary -- Chapter 5: No-Code Options for Building ML Models -- ML modeling options in Google Cloud -- What is AutoML? -- Vertex AI AutoML -- How to create a Vertex AI AutoML model using tabular data -- Importing data to use with Vertex AI AutoML -- Training the AutoML model for tabular/structured data -- Generating predictions using the recently trained model |
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Deploying a model in Vertex AI -- Generating predictions -- Generating predictions programmatically -- Summary -- Chapter 6: Low-Code Options for Building ML Models -- What is BQML? -- Getting started with BigQuery -- Using BQML for feature transformations -- Manual preprocessing -- Building ML models with BQML -- Creating BQML models -- Hyperparameter tuning with BQML -- Evaluating trained models -- Doing inference with BQML -- User exercise -- Summary -- Chapter 7: Training Fully Custom ML Models with Vertex AI -- Technical requirements -- Building a basic deep learning model with TensorFlow |
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Experiment -- converting black-and-white images into color images |
| Subject |
Google (Firm)
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Machine learning.
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Cloud computing.
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Artificial intelligence.
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Web services.
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Apprentissage automatique. |
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Infonuagique. |
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Intelligence artificielle. |
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Services Web. |
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artificial intelligence. |
| Genre |
Electronic books.
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| Added Author |
Chaudhary, Kartik, author.
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| Other Form: |
Print version: 9781801813327 |
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Print version: 1801815267 9781801815260 (OCoLC)1369678834 |
| ISBN |
9781801813327 (electronic bk.) |
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1801813329 (electronic bk.) |
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