WorkFusion Capgemini Wins WorkFusion’s Global Hack-the-Model Competition

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Capgemini Wins WorkFusion’s Global Hack-the-Model ML Competition

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Last Thursday, some of the top machine learning engineering talent in Southeast Asia spent the better part of their day in a neck-and-neck competition at WorkFusion’s Ascend conference in Bangalore, India. Their task was to build a machine learning model that extracts information from invoices using the WorkFusion ML SDK. The challenge had to be finished within 7.5 hours, a very short timeframe for such a complex task. This was our second Hackathon, after the premiere event at Ascend NYC in May where EPAM walked away with first prize.

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The competition started at 9am and finished at 4:30pm with teams representing each of these companies: Capgemini, Cognizant, Tech Mahindra, Infosys, DXC, Accenture, EPAM and Fujitsu.

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The teams were presented with a dataset that was split into three parts: training, validation and test. Training is the actual dataset used to train the model, validation is used to fine-tune the given model, and test provides the gold standard used to evaluate the model. It’s only used once the model is completely trained (using the training and validation sets). Raising the difficulty level was the realistic aspect that some of the datasets contained errors, such as missing fields, OCR errors and duplicate information.

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After an intense competition, where the top position kept fluctuating among the Capgemini, Accenture, DXC and EPAM teams, Capgemini took the grand prize of $1,500 with a final average score of 0.891709, which is a very impressive achievement considering the time crunch. Congratulations on a job well done!

To learn more about how RPA Express and AI-driven RPA can reduce cost, increase capacity, and improve service delivery throughout your business, visit WorkFusion.com.

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Capgemini Wins WorkFusion’s Global Hack-the-Model Competition was originally published in WorkFusion on Medium, where people are continuing the conversation by highlighting and responding to this story.

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