Overcome Last-Mile Challenges for AI Models
SoftServe improved governance and accelerated the time to deployment of AI models for one of the world’s largest custodian banks
AI/ML solutions give your bank a competitive advantage. They provide real-time analytics of enormous amounts of data to power AML/KYC, risk assessment, and fraud detection. However, the final stages of deployment into operational processes are a challenge that impedes the effectiveness of your AI models.
Delays in an analytic model’s journey from lab to production are costly. Streamline your AI/ML pipeline to accelerate time to market and see a quicker ROI.
Give AI model governance a boost
Our client is a global investment bank based in the U.S. and one of the largest custodian banks in the world. To improve risk modeling, security, and customer service, the bank needed to enhance its lifecycle management of AI models.
The workflow for AI/ML models involved disparate platforms, leading to:
- Inefficient development and deployment of AI/ML models
- Difficulty monitoring and optimizing models
Implementing a ModelOps platform would improve the bank’s governance, accountability, and monitoring of AI models.
Integrate ModelOps with legacy systems
The client needed to upgrade its systems to facilitate the deployment of its models. Integrating ModelOps with legacy platforms required:
- Updating existing infrastructure and tools
- Identifying a place for storing, operating, and monitoring models trained using GCP
Taking these steps increases the agility and scalability of legacy systems while streamlining deployment and enhancing governance.
Deploy AI models for cross-functional collaboration
SoftServe worked with partners at the bank to evaluate model performance by establishing a connection between the ModelOps platform and ground truth applications. We developed and implemented a publishing flow to transfer trained models and operate them using the ModelOps application, which accelerated deployment.
The solution extends the number of environments in which end users can train and run AI models, promoting alignment across teams. It also provides monitoring and governance through the ModelOps application.