This case study explores how a major US digital TV streaming company overcame challenges related to the optimization of their advertisement Campaigns. Our client needed a solution to identify optimal bids for serving ad impressions purchased through real-time auctions, occurring at a rate of 3 million requests per second.
The solution needed to address several different of challenges:
Blend's team developed a solution that automates ML model development for real-time scoring. By leveraging AWS services, Amazon EMR, Amazon Lambda, and Apache Spark, the team capabilities were extended to support low-latency scoring at the serving layer.
Framework-agnostic MLOps system leveraging MLFlow/ Extension of Apache Spark for low latency scoring/ Tensorflow.
The implementation of these solutions brought about remarkable benefits for the client. There was an impressive 80% reduction in cloud costs. Additionally, bid decisioning now occurs in less than 1 millisecond, ensuring swift and efficient ad serving. The ability to concurrently operate thousands of models through controlled A/B tests has enhanced the company's agility and decision-making capabilities. And lastly, there has been a notable 30% reduction in CPA, signifying improved cost-effectiveness and performance in advertising campaigns.