<span id="vlvnn"><dl id="vlvnn"></dl></span> <span id="vlvnn"><dl id="vlvnn"></dl></span><strike id="vlvnn"><dl id="vlvnn"><ruby id="vlvnn"></ruby></dl></strike>
<span id="vlvnn"><video id="vlvnn"><strike id="vlvnn"></strike></video></span>
<ruby id="vlvnn"><i id="vlvnn"></i></ruby>
<span id="vlvnn"></span><ruby id="vlvnn"><i id="vlvnn"></i></ruby>
<span id="vlvnn"></span>
<span id="vlvnn"><video id="vlvnn"></video></span>
<del id="vlvnn"><progress id="vlvnn"></progress></del>
<strike id="vlvnn"><i id="vlvnn"><del id="vlvnn"></del></i></strike><span id="vlvnn"></span>
<strike id="vlvnn"></strike>
<span id="vlvnn"></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%.

第一次俄罗斯破女初视频