Improved Operational Efficiency
Smart Insights and Visualization on the Performance of Models
Automated Production Model lifecycle management
Reducing the manual efforts for setup and configuration
The client is an Indian consumer goods company that primarily manufactures fashion accessories such as watches, jewelry, and eyewear.
They built Machine Learning models on local systems, which must be manually deployed and configured. The entire procedure took a long time, and the system was unscalable.
Amazon EC2
AWS Lambda
Amazon ECR
Amazon SageMaker
Amazon Systems Manager
Amazon S3
Amazon CodeCommit
Amazon RDS
Amazon API Gateway
Amazon Athena
Amazon QuickSight
AWS Glue
Apache Airflow
Quantiphi helped the client in the orchestration of training, evaluation, and deployment of over 100 models running on SageMaker by leveraging Apache Airflow.
Once each model is deployed, the Production Model Lifecycle is such that the scoring, evaluation, and retraining of the model is automated.
Quantiphi also helped the client in the visualization of the performance of the deployed models using Amazon QuickSight and Amazon Athena.
