Efficient MLOps Workflows with GCP Vertex Pipelines (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Efficient MLOps Workflows with GCP Vertex Pipelines" Efficient MLOps Workflows with GCP Vertex Pipelines is a comprehensive, hands-on guide for data scientists, machine learning engineers, and cloud architects seeking to operationalize AI at scale. Grounded in fundamental MLOps principles, this book meticulously explores the entire AI lifecycle on Google Cloud Platform, emphasizing scalability, security, reproducibility, and governance. Readers are introduced to Vertex AI and its powerful Vertex Pipelines, mastering platform capabilities while understanding best practices for automation and c...
"Efficient MLOps Workflows with GCP Vertex Pipelines" Efficient MLOps Workflows with GCP Vertex Pipelines is a comprehensive, hands-on guide for data scientists, machine learning engineers, and cloud architects seeking to operationalize AI at scale. Grounded in fundamental MLOps principles, this book meticulously explores the entire AI lifecycle on Google Cloud Platform, emphasizing scalability, security, reproducibility, and governance. Readers are introduced to Vertex AI and its powerful Vertex Pipelines, mastering platform capabilities while understanding best practices for automation and compliance-crucial for enterprise-grade machine learning solutions. From architectural patterns through pipeline orchestration, the book walks readers through sophisticated techniques for data engineering, experiment reproducibility, and collaborative workflow development. It covers robust strategies for automated data ingestion and transformation, feature store integration, and continuous data validation, ensuring data lineage and quality throughout the ML lifecycle. Practical patterns for modular component design, cost optimization, dependency management, and containerization provide the foundation for building portable, reusable workflows that are both efficient and resilient. With in-depth treatments of distributed model training, hyperparameter tuning, model validation, and CI/CD automation, the book empowers practitioners to deploy, monitor, and govern models in production with confidence. Advanced topics-such as hybrid and multi-cloud orchestration, event-driven pipelines, real-time and edge ML workflows, and integration with open-source MLOps tools-prepare readers for the rapidly evolving landscape of AI and machine learning. Whether for regulated industries or cutting-edge GenAI deployments, this book equips professionals to deliver, maintain, and innovate robust end-to-end MLOps solutions on Google Cloud.
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