德国电气与电子行业研发实力突出，目前拥有约9万名研发人员，70%的企业定期开展产品或流程创新，年均专利申请量达1.3万项，推动德国制造业1/3的创新成果落地。当前，德国正加快工业人工智能在电气与电子行业的应用，依托深厚的工业基础挖掘工业数据红利。企业层面，西门子研发工业人工智能产品与解决方案，推出智慧能碳管理平台Smart ECX，帮助企业监测能耗数据、管理碳足迹；博世集团从传统汽车供应商转型为领先的传感器、人工智能解决方案和互联产品供应商；软件企业SAP打造的平台，为物联网技术与工业应用的融合提供了支撑。
But that’s unironically a good idea so I decided to try and do it anyways. With the use of agents, I am now developing rustlearn (extreme placeholder name), a Rust crate that implements not only the fast implementations of the standard machine learning algorithms such as logistic regression and k-means clustering, but also includes the fast implementations of the algorithms above: the same three step pipeline I describe above still works even with the more simple algorithms to beat scikit-learn’s implementations. This crate can therefore receive Python bindings and even expand to the Web/JavaScript and beyond. This also gives me the oppertunity to add quality-of-life features to resolve grievances I’ve had to work around as a data scientist, such as model serialization and native integration with pandas/polars DataFrames. I hope this use case is considered to be more practical and complex than making a ball physics terminal app.
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