I-Corps: A Framework for Streamlining the Development and Deployment of Generative Artificial Intelligence (AI) Models on Enterprise Data

I-Corps项目的广泛影响和商业潜力在于开发人工智能（AI）平台，使数据科学家和研究人员能够更快、更轻松地部署人工智能。目前，这些模型的开发和部署通常需要大量的数据、时间和财务资源，这对许多组织来说是具有挑战性的，尤其是中小型企业。提出的技术是一个人工智能框架，将先进的人工智能技术与现实世界的实际应用相结合。它旨在简化大型语言模型的创建和部署，并擅长基于企业数据构建生成模型，同时确保对数据保护和生成的严格保障。此外，提议的平台提供灵活的部署选项，在公共云平台上高效运行，以实现成本效益和可扩展性，或者在私有云或本地服务器上运行，为优先考虑数据隐私和安全性的组织提供服务。数据科学家和研究人员可以使用该技术来简化模型开发，包括数据采集、操作、管理、特征生成、超参数调优和部署，从而基于他们的自定义数据集有效地构建和部署最先进的人工智能建模。I-Corps项目的基础是开发一个软件平台，用于简化生成式人工智能（AI）模型的部署，包括多模态大型语言模型。提出的技术利用基于研究的数据工程和机器学习软件库，包括数据插件，服务应用程序编程接口，以及具有人类反馈和参数优化的强化学习，以简化复杂的数据过程。此外，利用人类反馈的强化学习和监督微调来增强人工智能模型的性能，提高安全性和保障措施，使这些模型的部署更安全、更可靠。拟议的技术旨在在公共云平台或私有云/本地服务器上运行，优先考虑企业的数据隐私和安全。专注于保护大型语言模型符合社会对安全和值得信赖的人工智能应用程序的要求。提议的平台使用用户友好的界面和数据插件，用于在单个DGX服务器上进行数据采集、注释、超参数调优、微调万亿参数模型等任务，用于图形处理单元（GPU）调度，以在云系统或承诺的网络基础设施上进行模型训练和操作部署。通过自动化这些任务和简化开发工作流程，该平台减少了技术障碍，节省了时间，并降低了成本。这可以实现高效的数据处理，并为各种专业人员和研究人员提供先进的人工智能开发机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact and commercial potential of this I-Corps project is the development of an artificial intelligence (AI) platform to streamline and enable data scientists and researchers to deploy AI faster and more easily. Currently, the development and deployment of these models often require substantial data, time, and financial resources, which can be challenging for many organizations, particularly small and medium-sized enterprises. The proposed technology is an AI framework that integrates advanced AI technologies with practical real-world applications. It is designed to streamline the creation and deployment of large language models and is adept at building generative models based on enterprise data while ensuring stringent safeguards for data protection and generation. In addition, the proposed platform provides flexible deployment options, functioning efficiently on public cloud platforms for cost-effectiveness and scalability, or within private clouds or on-premises servers for organizations that prioritize data privacy and security. The proposed technology may be used by data scientists and researchers to simplify model development, including data acquisition, manipulation, curation, feature generation, hyperparameter tuning, and deployment, to efficiently build and deploy state-of-the-art AI modeling based on their custom datasets.This I-Corps project is based on the development of a software platform for streamlining the deployment of generative Artificial Intelligence (AI) models including multimodal large language models. The proposed technology leverages research-based data engineering and machine learning software libraries, including data-plugins, service application programming interfaces, and reinforcement learning with human feedback and parameter optimization to simplify complex data procedures. In addition, reinforcement learning with human feedback and supervised fine tuning are used to enhance the performance of AI models and improve the security and safeguards, to make the deployment of these models safer and more reliable. The proposed technology is designed to operate on public cloud platforms or private clouds/on-premises servers, which prioritizes data privacy and security for enterprises. A focus on safeguarding large language models aligns with societal imperatives for secure and trustworthy AI applications. The proposed platform uses a user-friendly interface and data plugins for tasks like data acquisition, annotation, hyperparameter tuning, fine-tuning trillion parameter models on a single DGX server for graphics processing unit (GPU) scheduling to conduct model training and operating deployment on cloud systems or on on-promise cyberinfrastructure. By automating these tasks and streamlining development workflow, the platform reduces technical hurdles, saves time, and cuts costs. This enables efficient data processing and broadens access to advanced AI development for various professionals and researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.