Legacy models processed
Reduction in scoring time
Reduced manual effort
A leading marketing data & technology services company that provides the data foundation for the world’s best marketers, had terabytes of data residing in its on-premises systems, compelling them to perform manual ML operations. They wanted a machine learning lifecycle management framework that could automate the manual process of managing the training and inference jobs, as well as reduce the turnaround time to run the scoring using their on-premise infrastructure.
Amazon S3
Amazon SageMaker
Amazon RDS
Amazon EMR
Amazon CloudWatch
AWS CloudTrail
AWS CloudFormation
Quantiphi developed a framework that could manage the lifecycle of machine learning models in production and the incoming data to different models. An automated training and inference end-to-end pipeline was also built in a fully configured environment with minimal manual intervention; enabling users to pre-process, build, train, tune, and score the models.
