Reinforcement Learning of a deep neural network has been applied to the problem of supply chain logistics: In a stochastic environment, how to optimize pickup and delivery schedules.
Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.
Graph convolutional networks exhibit optimal deep learning on big graph data to gain business insight.
Graphs and graph datasets are rich data structures that can be used uniquely to improve the accuracy and effectiveness of machine learning workflows. Some of the key interactions are graph analytics as features, semi supervised learning, graph based deep learning, and machine learning approaches to hard graph problems.
MyHouseby wanted users to love the experience of discovering and customizing their new home in new ways using new technologies.
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The data over fifty years of operation was messy and segregated, so the regional planners often relied on localized tribal knowledge to get customers product in time to meet SLAs. This intuition-driven delivery mechanism was ripe for system wide optimization to improve overall efficiency and reduce network costs.
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