GoogleCloudPlatform/generative-ai

Features

• Generative AI Models - 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.
• Generative AI Development - A unified environment for accessing, fine-tuning, and deploying large language models with integrated safety and evaluation tools.
• Unified Model Interfaces - Connecting to and testing diverse artificial intelligence models through a unified interface to build custom applications for specific business needs.
• Model Deployment Management - 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.
• Managed Hosting Services - A cloud-based infrastructure for hosting and scaling generative models without the need for manual server maintenance or configuration.
• Automated Output Evaluation - A suite of automated testing services and rubrics for assessing the quality and safety of outputs from generative artificial intelligence.
• Orchestrators - A framework for managing the lifecycle of artificial intelligence models including deployment, performance monitoring, and content filtering.
• Model Fine-Tuning - 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.
• Model Abstraction Layers - Provides a consistent programming interface to interact with diverse artificial intelligence models regardless of their underlying architecture or provider.
• Safety and Alignment Frameworks - 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.
• LLM Evaluation - Uses programmatic rubrics and testing services to measure the accuracy and relevance of model outputs against defined project benchmarks.
• Model Evaluation - 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.
• AI Evaluation Frameworks - Assessing the performance and reliability of generated content using automated testing services and rubrics to ensure alignment with project requirements.
• Fine-Tuning Pipelines - Adjusts pre-trained model weights using domain-specific datasets to improve performance on specialized tasks and custom business requirements.
• Vector Embeddings - Transforms unstructured data into high-dimensional numerical representations to improve the retrieval and contextual understanding of domain-specific knowledge.
• Cloud Service Orchestrations - Automates the provisioning and scaling of cloud compute resources to host machine learning models as reliable and accessible web services.
• AI Content Filters - Intercepts and inspects model responses against predefined safety guidelines to prevent the generation of harmful or non-compliant content.
• Cloud AI Platforms - Notebooks and code for developing applications with cloud-based generative models.
• LLM Development and Research - Sample code and notebooks for cloud-based generative AI.
• Model Implementations - Offers end-to-end examples for deploying and using models on cloud infrastructure.
• Development Utilities - Tools and patterns for implementing advanced retrieval strategies.

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