<strike id="h1hft"><dl id="h1hft"></dl></strike>
<span id="h1hft"></span>
<strike id="h1hft"></strike><strike id="h1hft"><video id="h1hft"></video></strike>
<strike id="h1hft"></strike>
<th id="h1hft"><video id="h1hft"><strike id="h1hft"></strike></video></th>
<span id="h1hft"></span>
<progress id="h1hft"><noframes id="h1hft"><strike id="h1hft"></strike>
<span id="h1hft"></span>
<strike id="h1hft"></strike>
<span id="h1hft"><video id="h1hft"></video></span><span id="h1hft"></span>

APPROACH

To achieve this, we leveraged Sancus in the following aspects:

  • Data Engineering: Processed, cleansed and passed a high volume of data for approximately 3 million SKUs through the text mining pipeline
  • Neural Networks: Developed an ML algorithm using elements of supervised and unsupervised learning to classify the remaining SKUs based on existing classifications
  • Deployment: This ML based product classification solution was implemented on the cloud using Microsoft Azure

KEY BENEFITS

  • The solution allowed the client to achieve product to category classification at scale with higher accuracies, providing better insights into revenue and sales opportunity

RESULTS

  • The monthly classification throughput increased by 28x and the total accuracy of product classification shot up to 95%.

三级网址