# googlecloudplatform/generative-ai **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/googlecloudplatform-generative-ai).** 12,700 stars · 3,713 forks · Jupyter Notebook · apache-2.0 ## Links - GitHub: https://github.com/GoogleCloudPlatform/generative-ai - Homepage: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview - awesome-repositories: https://awesome-repositories.com/repository/googlecloudplatform-generative-ai.md ## Topics `agents` `gcp` `gemini` `gemini-api` `gen-ai` `generative-ai` `google` `google-cloud` `google-gemini` `langchain` `large-language-models` `llm` `vertex-ai` `vertex-ai-gemini-api` `vertexai` ## Description This project is a development platform for managing the lifecycle of generative artificial intelligence models. It provides a unified environment for accessing, fine-tuning, and deploying large language models, serving as an orchestrator that handles the integration of diverse models into custom applications. The platform distinguishes itself by offering a managed infrastructure for hosting and scaling models, which removes the requirement for manual server maintenance or configuration. It includes integrated tools for supervised fine-tuning and vector embedding optimization, allowing for the refinement of model performance to meet specialized domain requirements. The framework incorporates comprehensive capabilities for monitoring and governance, including automated quality evaluation services that use programmatic rubrics to assess output accuracy. It also enforces responsible artificial intelligence standards through policy-driven content filtering, ensuring that generated responses remain aligned with established safety and ethical guidelines. The repository provides a collection of Jupyter Notebooks that serve as documentation and implementation guides for these development and deployment workflows. ## Tags ### Artificial Intelligence & ML - [Generative AI Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-models.md) — Connect to diverse artificial intelligence models through a unified interface to discover, test, and deploy various first-party or open-weights solutions for your specific application requirements. ([source](https://cloud.google.com/vertex-ai/generative-ai/docs)) - [Generative AI Development](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai-development.md) — A unified environment for accessing, fine-tuning, and deploying large language models with integrated safety and evaluation tools. - [Unified Model Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-to-text-integrations/unified-model-interfaces.md) — Connecting to and testing diverse artificial intelligence models through a unified interface to build custom applications for specific business needs. - [Managed Hosting Services](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/ai-model-management/managed-hosting-services.md) — A cloud-based infrastructure for hosting and scaling generative models without the need for manual server maintenance or configuration. - [Automated Output Evaluation](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-output-evaluation.md) — A suite of automated testing services and rubrics for assessing the quality and safety of outputs from generative artificial intelligence. - [Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/large-language-models/orchestrators.md) — A framework for managing the lifecycle of artificial intelligence models including deployment, performance monitoring, and content filtering. - [Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning.md) — Adjust model performance by applying fine-tuning and preference alignment to ensure the system produces accurate results tailored to the specific needs of your unique domain tasks. ([source](https://cloud.google.com/vertex-ai/generative-ai/docs)) - [Model Abstraction Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-abstraction-layers.md) — Provides a consistent programming interface to interact with diverse artificial intelligence models regardless of their underlying architecture or provider. - [Safety and Alignment Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/safety-and-alignment-frameworks.md) — Apply content filtering and responsible guidelines to monitor and restrict model outputs, ensuring that all generated responses adhere to established safety policies and ethical usage standards. ([source](https://cloud.google.com/vertex-ai/generative-ai/docs)) - [AI Evaluation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/ai-evaluation-frameworks.md) — Assessing the performance and reliability of generated content using automated testing services and rubrics to ensure alignment with project requirements. - [Fine-Tuning Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/fine-tuning-pipelines.md) — Adjusts pre-trained model weights using domain-specific datasets to improve performance on specialized tasks and custom business requirements. - [Vector Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings.md) — Transforms unstructured data into high-dimensional numerical representations to improve the retrieval and contextual understanding of domain-specific knowledge. ### DevOps & Infrastructure - [Model Deployment Management](https://awesome-repositories.com/f/devops-infrastructure/model-deployment-management.md) — Deploy partner and open-source models as accessible web services to eliminate the need for manual infrastructure management, server maintenance, and complex scaling operations for your applications. ([source](https://cloud.google.com/vertex-ai/generative-ai/docs)) - [Cloud Service Orchestrations](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure/operational-monitoring-governance/cloud-service-orchestrations.md) — Automates the provisioning and scaling of cloud compute resources to host machine learning models as reliable and accessible web services. ### Testing & Quality Assurance - [LLM Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/llm-evaluation.md) — Uses programmatic rubrics and testing services to measure the accuracy and relevance of model outputs against defined project benchmarks. - [Model Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/model-evaluation.md) — Assess generated content using automated testing services and adaptive rubrics to ensure that all model responses maintain high quality and remain aligned with your specific project requirements. ([source](https://cloud.google.com/vertex-ai/generative-ai/docs)) ### Security & Cryptography - [AI Content Filters](https://awesome-repositories.com/f/security-cryptography/content-filtering/ai-content-filters.md) — Intercepts and inspects model responses against predefined safety guidelines to prevent the generation of harmful or non-compliant content. ### Part of an Awesome List - [Cloud AI Platforms](https://awesome-repositories.com/f/awesome-lists/ai/cloud-ai-platforms.md) — Notebooks and code for developing applications with cloud-based generative models. - [LLM Development and Research](https://awesome-repositories.com/f/awesome-lists/ai/llm-development-and-research.md) — Sample code and notebooks for cloud-based generative AI. - [Development Utilities](https://awesome-repositories.com/f/awesome-lists/devtools/development-utilities.md) — Tools and patterns for implementing advanced retrieval strategies.