One of the retailer clients was having difficulty in processing a very large volume of data from different sales channels. Their hadoop based infrastructure was unable to implement the process successfully. The client wanted to have a single source of data and also to increase the processing speed while deploying the various algorithms to make effective models.
CASE STUDIES
AI enabled system to process millions of unstructured data for a retailer
SUMMARY
METHODOLOGY
• Shifting of Hadoop based infrastructure to Google Cloud Platform
• Using the GCP infrastructure to help the data scientists in a cost effective way
• Merging of various parameters with regard to customer
TAKE AWAYS
• Cost efficient and auto scalable infrastructure
• Managing a wide range of ML models
END RESULTS
• A huge volume of data being processed in a given time frame.
• Increase in the speed of processing.
• Cost reduction by more than 30%.