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

这个I-Corps项目的更广泛的影响和商业潜力是开发一个人工智能（AI）平台，以简化和使数据科学家和研究人员能够更快，更容易地部署AI。 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，这对许多组织来说可能是一个挑战，特别是中小型企业。所提出的技术是一个AI框架，将先进的AI技术与实际的现实世界应用相结合。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，拟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，或者在私有云或内部部署服务器中为优先考虑数据隐私和安全的组织提供服务。数据科学家和研究人员可以使用所提出的技术来简化模型开发，包括数据获取、操纵、策展、特征生成、超参数调整和部署。基于他们的定制数据集，高效地构建和部署最先进的人工智能建模。这个I-Corps项目是基于开发一个软件平台，用于简化生成式人工智能（AI）的部署。多模态大型语言模型。 所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带有人工反馈和参数优化的强化学习，以简化复杂的数据过程。 此外，还使用带有人工反馈的强化学习和监督微调来增强AI模型的性能，提高安全性和保障措施，使这些模型的部署更加安全可靠。 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性。专注于保护大型语言模型符合安全和值得信赖的AI应用程序的社会需求。拟议的平台使用用户友好的界面和数据插件，用于数据采集，注释，超参数调整，微调单个DGX服务器上的万亿参数模型，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并简化开发工作流程，该平台减少了技术障碍，节省了时间并降低了成本。该奖项反映了NSF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.