vertex-ai-samples and applied-ai-engineering-samples

These are ecosystem siblings—both official Google Cloud repositories providing complementary sample collections for the same Vertex AI platform, with the former offering broader ML/AI workflows and the latter focusing specifically on generative AI implementations.

Maintenance 20/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 6/25
Adoption 10/25
Maturity 16/25
Community 25/25
Stars: 663
Forks: 258
Downloads:
Commits (30d): 27
Language: Jupyter Notebook
License: Apache-2.0
Stars: 830
Forks: 213
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
No Package No Dependents
No Package No Dependents

About vertex-ai-samples

GoogleCloudPlatform/vertex-ai-samples

Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.

This repository provides practical examples and guided notebooks for anyone building or managing machine learning and generative AI solutions on Google Cloud's Vertex AI platform. It takes your raw data and ideas, and through step-by-step code, helps you create, deploy, and manage AI models. Data scientists, machine learning engineers, and AI practitioners can use these resources to accelerate their projects.

machine-learning-operations generative-ai-development cloud-ml-platform model-deployment data-science-workflow

About applied-ai-engineering-samples

GoogleCloudPlatform/applied-ai-engineering-samples

This repository compiles code samples and notebooks demonstrating how to use Generative AI on Google Cloud Vertex AI.

This repository provides examples, guides, and code samples to help developers and machine learning engineers build applications using Google Cloud's Generative AI tools, especially on Vertex AI. It helps you understand how to use large language models (LLMs) for tasks like creating marketing content, improving customer service, or building chatbots that can interact with your databases. You'll find practical examples of applying AI to real business problems.

Generative AI development Machine Learning Engineering Cloud AI solutions Application development Data interaction

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