Generative AI on Google Cloud: From Ideation to Production (Early Release)

Learn a comprehensive approach to building and deploying generative AI and large language models on Google Cloud, with a focus on language, images, and other modalities. Authors Mona Mona and Rajesh Thallam cover the latest launches from the Google Cloud Vertex AI platform, including PaLM2, Imagen, Codey, embeddings, Model Garden, and Generative AI App Builder.

AI developers, data scientists, and ML engineers will work with hands-on labs to implement these models in various business contexts, as well as with architecture blueprints for building and deploying generative AI applications for production use. You'll learn challenges and considerations associated with operationalizing these models using Google Cloud to adapt to business use cases and applications.

This guide helps you

Build generative AI use cases on Google Cloud and distinguish Google developer offerings from Google Cloud Enterprise offerings
Explore the generative AI lifecycle with Vertex AI features: Model Garden, Vertex AI Studio, and Vertex AI Platform
Train, fine-tune, and deploy Googleâ??s foundation models and open source models on the cloud
Understand the importance of reinforcement learning with human feedback for aligning LLMs with human preferences
Examine model architectures of data modalities such as images, code, audio, and multimodality
Learn how generative AI changes or augments MLOps using Vertex AI Platform, including pipelines
Understand the role of responsible AI when building generative AI applications on Google Cloud