Increasing the accuracy of analytics technologies drives modern business, and getting more realistic results applicable to on-the-ground business operational conditions means orgs can make better decisions faster.
Reinforcement Learning at scale; Scheduling thousands of vehicles in multiple environments - how we made it work.
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 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.
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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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