![case study](https://cdn.quantiphi.com/2024/02/a21460f7-machine-learning-lifecycle-management.jpg)
Machine Learning Lifecycle Management
Banking & Financial ServicesBusiness Impacts
Reduction in human intervention
5 - 2 days
Reduction in provisioning time
Improvement in customer satisfaction
Customer Key Facts
- Location : North America
- Industry : Financial Services
Problem Context
The customer, an international financial services company, provides a diverse range of wealth accumulation and protection products and services. Their existing process for data preparation, model selection, and testing was manually driven and riddled with inefficiencies; taking up to five days in resource provisioning.
Challenges
- Integrating with third party solutions to automate the process of Amazon SageMaker resource deployment
- Automating the process from data preparation to model selection and testing on Amazon SageMaker
![](https://cdn.quantiphi.com/2024/02/d224141f-challenges_machine-learning-lifecycle-managementmachine-learning-lifecycle-management.png)
Technologies Used
![Amazon SageMaker](https://cdn.quantiphi.com/2024/02/50f0a997-arch_amazon-sagemaker_64@5x-1.png)
Amazon SageMaker
![AWS CloudFormation](https://cdn.quantiphi.com/2024/02/AWS-CloudFormation@4x.png)
AWS CloudFormation
![Amazon Simple Storage Service](https://cdn.quantiphi.com/2024/02/9c40e5d8-amazon-s3.png)
Amazon Simple Storage Service
Automating Machine Learning Lifecycle Management for Increased Efficiency and Cost Savings
The customer sought to automate the process of providing Amazon SageMaker resources to end users; better enabling its developers and data scientists to create, train, and deploy machine-learning models in the cloud.
Solution
Quantiphi developed predefined cloud formation templates (CFTs) that deployed the necessary Amazon SageMaker resources based on a data scientist’s requirements. The solution includes a model leaderboard for performance evaluation, and uses a combination of custom-built pieces as well as the Autopilot, Automatic Model Tuning, Endpoints, and Model Monitor features of Amazon SageMaker.
Result
- Automated model training
- Reduced manual intervention
- Cost reduction