Improving customer service with predictive models for IBM London Software Lab

IBM加拿大伦敦软件实验室旨在为全球客户的物联网(IoT)产品提供卓越、专业的服务和产品。然而，他们发现在尝试帮助客户进行性能趋势预测和硬件规模调整建议时具有挑战性。具体地说，他们的客户的交易和硬件与公司的内部基准测试有很大不同。现有的实用程序还不能准确地模拟这些差异。为此，IBM加拿大伦敦软件实验室迫切需要新的机器学习技术来构建预测模型，并创建分析系统来帮助客户进行硬件规模调整、性能趋势分析和未来数据预测任务。 我和我的研究生们提出了先进的机器学习解决方案，以利用IBM伦敦实验室的客户数据，建立准确的预测模型。具体地说，我们计划使用几种最先进的机器学习算法来解决特征选择、模型评估和优化方面的技术挑战。我们的解决方案可能有助于改善IBM伦敦实验室的客户服务。随着我们与IBM伦敦合作，为他们在美国/加拿大的客户提供更好的物联网硬件规模和性能趋势服务，我们将为加拿大人带来直接和间接的经济和社会效益。

IBM Canada London Software Lab aims to provide excellent, professional services and products to customers around the world for their IoT (Internet of Things) products. However, they find it challenging when trying to help their clients with performance trend prediction and hardware sizing recommendation. Specifically, their customers have transactions and hardware that differ greatly compared to the company's internal benchmark testing. There are no existing utilities that can accurately model these differences. For this reason, IBM Canada London Software Lab urgently needs novel machine learning technologies to build predictive models, and create an analytic system to assist customers with hardware sizing, performance trending and future data prediction tasks. My graduate students and I have proposed advanced machine learning solutions to make use of the client data of IBM London Lab and build accurate predictive models. Specifically, we plan to address technical challenges in feature selection, model evaluation and optimization using several state-of-the-art machine learning algorithms. Our solution will potentially help to improve the customer service of IBM London Lab. As we work with IBM London to provide better IoT hardware sizing and performance trending services to their clients in USA/Canada, we will generate direct and indirectly economic and social benefits for Canadians.