金融机构在人工智能领域有着悠久的历史. Statisticians used hand-coded heuristics and expert systems to detect money laundering schemes and execute high frequency trades. But those older systems are brittle and don’t adapt well to black swan events and fast changing circumstances. That’s why financial leaders everywhere are turning to AI/ML to stop fraud dead in its tracks, upgrade their trading platforms and get their customers help before they ever need to talk to a support representative. Machine learning is highly flexible and it can find fraud patterns that old heuristic systems miss, 梳理交易之间隐藏的关系. 它可以提供更好的服务, more human-like customer support and it can create trading systems that can respond to sudden shifts in the market faster.
What if there was an open data science platform that tracked every change in your data, 模型, code and did everything with the same discipline that banks track their investment?
这就是厚皮动物的原因. Our powerful machine learning platform lets anyone transform ad-hoc model creation into automated repeatable processes regardless of the format. 厚皮类动物 pipelines enable teams to collaborate more effectively and it’s robust data transformation engine delivers the data foundation you need to build your machine learning pipelines on.
Financial institutions face a complex and myriad set of regulations and compliance frameworks. 通常那些遵从性标准是重叠和冲突的. Machine learning offers unprecedented promise and possibility but it also brings new compliance challenges.
旧的启发式和手工编码规则更容易调试. 但有了机器学习，你的模型从数据本身学习. 如果你不知道这些数据来自哪里, 谁在什么时候碰过它, 你很容易就会发现自己陷入困境. 在旅途的每一步, 从数据摄取到将模型投入生产, 你需要知道实现目标的步骤. You need to be able to roll backwards and forwards in time to recreate any step or answer any question from a regulatory agency.
厚皮类动物 can reduce the time it takes for auditors) to understand that journey from data to model, 通过提供每个步骤的文档. With simple command `pachctl inspect` you can trace the entire journey of how your data became a model and prove every step in between. 无论是为了调试目的, 跨业务单元共享数据科学工作流程, 或者满足数据遵从性需求, 每个人都需要知道, 有信心, 任何模型, 工作流, or result can be traced back to its original source with fully reproducible steps.
Build your own fully automated, end-to-end market sentiment analysis pipeline for FREE
Try out this end-to-end Market Sentiment analysis example using NLP on 厚皮类动物 Hub for FREE. Included are step-by-step instructions on building a fully automated end-to-end machine learning pipeline from raw data to a deployed model with complete data lineage. 沿着这条路, 您将学习如何合并数据标记, 转移学习, 模型的监控, 如何自动处理新数据, 和更多的.
模型风险应该像其他类型的风险一样加以管理, 模型风险随着模型复杂性的增加而增加, 输入和假设的不确定性更高, 更广泛的使用, 以及更大的潜在影响.
Banks should identify the sources of risk and assess the impact across a number of different fairness, 道德准则尽可能减少这种威胁.
Banks should consider risk from individual 模型 and in the aggregate. Aggregate model risk is affected by interaction and dependencies among 模型; reliance on common assumptions, data, 或方法. 厚皮类动物 was engineered to help resolve this problem by letting you see every transformation your data, 代码和模型贯穿机器学习的整个生命周期.
厚皮类动物 delivers the strong data foundation you need to create and maintain the right governance, 政策, 控制你的数据. With 厚皮类动物 you can build end-to-end pipelines where everything is tracked and versioned, which makes supporting your auditing and compliance teams and internal audit and compliance functions that much easier.