Vertex AI in Production: End-to-End ML and Generative AI Deployment on Google Cloud - Softcover

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Build, deploy, scale, and manage production-ready machine learning and generative AI systems using Google Cloud Vertex AI.

Artificial intelligence is rapidly moving from experimentation to production. Organizations today need more than isolated machine learning models—they need scalable AI platforms capable of training, deploying, monitoring, securing, and operationalizing AI systems across real-world enterprise environments.

Google Cloud Vertex AI has emerged as one of the most powerful unified AI platforms for building modern machine learning and generative AI applications at scale.

Vertex AI in Production is a practical, end-to-end guide for developers, machine learning engineers, MLOps professionals, cloud architects, data scientists, and enterprise AI teams who want to move beyond prototypes and build production-grade AI systems on Google Cloud.

Learn how to use Vertex AI for:

• Machine learning model development
• Generative AI application deployment
• MLOps automation
• AI workflow orchestration
• Real-time and batch inference
• Model monitoring and governance

Understand how Vertex AI simplifies the full machine learning lifecycle from experimentation to production deployment.

Learn how to:

• Prepare and manage datasets
• Train machine learning models
• Automate feature engineering
• Deploy scalable prediction endpoints
• Build reproducible ML workflows
• Implement CI/CD for machine learning

Includes practical examples using real-world enterprise deployment patterns.

Explore modern generative AI development using:

• Gemini models
• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• Vector search
• AI agents and orchestration
• Enterprise AI assistants

Learn how to build secure and scalable AI-powered applications using Vertex AI and Google Cloud infrastructure.

Master enterprise-grade AI operations including:

• Vertex AI Pipelines
• Model Registry
• Experiment tracking
• Automated training workflows
• Kubernetes and containerized deployment
• Infrastructure scaling and optimization

Understand how leading organizations operationalize AI systems reliably at scale.

Learn best practices for:

• Model monitoring and drift detection
• AI observability
• Responsible AI implementation
• Data governance and compliance
• IAM and security architecture
• Cost optimization and performance tuning

Perfect for organizations deploying AI in regulated and high-scale environments.

Build practical production systems including:

• Generative AI chat applications
• Recommendation engines
• Intelligent document processing systems
• Predictive analytics platforms
• Enterprise AI search systems
• Real-time ML inference pipelines

Each chapter focuses on practical implementation strategies and production-oriented architecture guidance.

From machine learning pipelines and generative AI deployment to MLOps automation and enterprise AI governance, this is your complete guide to mastering Vertex AI in production.

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