Trubrics Beta Release

** 该项目的目标是开发我们的AI保证解决方案和业务Trubrics，用于Beta版本。** Trubrics增强了业务和数据科学团队之间的协作和信任。这增加了参与AI项目的人员的多样性-提高了AI系统的可靠性，并减少了偏见。企业内的机器学习（ML）项目旨在支持业务团队/领域专家进行关键业务决策。然而，由于缺乏业务团队的信任，企业正在努力增加ML项目的采用。影响ML计划的整体投资回报率。这是由于缺乏技术理解造成的，并且ML具有固有的风险：* ML模型难以解释 * 性能会随着时间的推移而下降 * 没有经过验证的跟踪记录AI法规的引入，例如欧盟AI法案，增加了AI项目的合规风险。这些法规的重点是降低“高风险”用例的风险，如信用评分--影响英国的主要行业，如金融服务和制药业。高风险模型必须符合关键要求，包括高质量的数据、适当的文档、透明度、人为监督以及准确性、稳健性和偏差测试。Trubrics将提供一个解决方案，以保持领先于这些法规。Trubrics为业务团队提供了一个解决方案，以提交ML模型的定性和定量反馈。这些反馈被数据科学家用来改进和验证模型。Trubrics还提供了一个解决方案来开发ML验证（测试）的“黄金标准”。这将自动执行验证过程，并确保验证是可重复的，并记录以供审计。验证包括数据质量;模型性能、稳健性和偏差测试以及自定义业务验证。在过去的10个月里，联合创始人自筹资金开发了Trubrics MVP。包括用于收集用户反馈和创建验证的SDK;以及用于存储、管理和可视化反馈和验证的软件即服务（SaaS）平台。抢先体验计划目前正在进行中，以与初始用户一起测试该解决方案。用户看到了解决方案的价值，但是 ** 要将解决方案和业务推向Beta版本，还需要进行关键的技术和商业开发。**本项目旨在完成Beta版所需的开发，并 ** 将产品推向市场。**

**The goal of this project is to develop our AI Assurance solution and business, Trubrics, for a Beta release.**Trubrics increases collaboration and trust between business and data science teams. This increases the diversity of the people involved in AI projects - increasing the reliability of the AI system, and reduces bias.Machine learning (ML) projects within businesses are developed to support business teams/domain experts with key business decisions. However, businesses are struggling to increase the adoption of ML projects, due to a lack of trust from the business teams. Impacting the overall ROI of ML initiatives.This is caused by a lack of technical understanding, and ML being inherently risky:* ML models are difficult to explain* Performance can degrade overtime* No proven track recordThe introduction of AI regulations, such as the EU AI Act, increases the compliance risk of AI projects. These regulations are focused on reducing the risk of 'high-risk' use cases, such as credit scoring - impacting key UK industries, such as Financial Services and Pharmaceutical.High-risk models must align with key requirements, including high-quality data, appropriate documentation, transparency, human oversight, and testing for accuracy, robustness and bias. Trubrics will provide a solution to stay ahead of these regulations.Trubrics provides a solution for business teams to submit qualitative and quantitative feedback for ML models. This feedback is used by data scientists to improve and validate the models.Trubrics also provides a solution to develop a 'gold standard' of ML validations (tests). This automates the validation process, and ensures validations are repeatable and logged for audit. Validations include data quality; model performance, robustness and bias tests, and custom business validations.Over the last 10 months, the Co-Founders have self-funded the development of the Trubrics MVP. Including an SDK to collect user feedback, and create validations; and a software-as-a-service (SaaS) platform to store, manage and visualise both feedback and validations.An Early Access program is currently underway, to test the solution with initial users. The users see value in the solution, however **there are key pieces of technical and commercial development required to take the solution and business to Beta release.**This project aims to complete the development required for the Beta release, and **take the product to market.**