AI and machine learning resources

Last reviewed 2024-11-22 UTC

The Architecture Center provides content resources across a wide variety of AI and machine learning subjects. This page provides information to help you get started with generative AI, traditional AI, and machine learning. It also provides a list of all the AI and machine learning (ML) content in the Architecture Center.

Get started

The documents listed on this page can help you get started with designing, building, and deploying AI and ML solutions on Google Cloud.

Explore generative AI

Start by learning about the fundamentals of generative AI on Google Cloud, on the Cloud documentation site:

To explore a generative AI and machine learning blueprint that deploys a pipeline for creating AI models, see Build and deploy generative AI and machine learning models in an enterprise. The guide explains the entire AI development lifecycle, from preliminary data exploration and experimentation through model training, deployment, and monitoring.

Browse the following example architectures that use generative AI:

For information about Google Cloud generative AI offerings, see Vertex AI and running your foundation model on GKE.

Design and build

To select the best combination of storage options for your AI workload, see Design storage for AI and ML workloads in Google Cloud.

Google Cloud provides a suite of AI and machine learning services to help you summarize documents with generative AI, build image processing pipelines, and innovate with generative AI solutions.

Keep exploring

The documents that are listed later on this page and in the left navigation can help you build an AI or ML solution. The documents are organized in the following categories:

  • Generative AI: Follow these architectures to design and build generative AI solutions.
  • Model training: Implement machine learning, federated learning, and personalized intelligent experiences.
  • MLOps: Implement and automate continuous integration, continuous delivery, and continuous training for machine learning systems.
  • AI and ML applications: Build applications on Google Cloud that are customized for your AI and ML workloads.

AI and machine learning resources in the Architecture Center

You can filter the following list of AI and machine learning resources by typing a product name or a phrase that's in the resource title or description.

Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build

Best practices for implementing machine learning on Google Cloud

Build an ML vision analytics solution with Dataflow and Cloud Vision API

Build and deploy generative AI and machine learning models in an enterprise

Cross-silo and cross-device federated learning on Google Cloud

Data science with R on Google Cloud: Exploratory data analysis

Deploy and operate generative AI applications

Design storage for AI and ML workloads in Google Cloud

Geospatial analytics architecture

Google Workspace Backup with Afi.ai

Guidelines for developing high-quality, predictive ML solutions

Infrastructure for a RAG-capable generative AI application using GKE

Infrastructure for a RAG-capable generative AI application using Vertex AI

Jump Start Solution: AI/ML image processing on Cloud Functions

Jump Start Solution: Analytics lakehouse

Jump Start Solution: Data warehouse with BigQuery

Jump Start Solution: Generative AI document summarization

Jump Start Solution: Generative AI Knowledge Base

Jump Start Solution: Generative AI RAG with Cloud SQL

MLOps: Continuous delivery and automation pipelines in machine learning

Model development and data labeling with Google Cloud and Labelbox

Serve Spark ML models using Vertex AI

Use generative AI for utilization management

Use Vertex AI Pipelines for propensity modeling on Google Cloud