Scalable Machine Learning Solutions

Goals

Our client, offering a B2B SaaS product for real-time price optimization, faced the challenge of elevating their existing ML models to a production-ready state. The primary objective was to architect a scalable and efficient MLOps framework that could support their current and future ML endeavors.

Challenges

Lack of Scalability: The existing setup was not scalable, posing significant challenges in managing resource duplication and system complexity as ML usage expanded. Non-adherence to best practices.

Integration Issues: The initial iteration, built with SageMaker pipelines, hindered seamless integration with services outside the SageMaker ecosystem.

Infrastructure Management: The need for a system that empowered model creators without overburdening them with the complexities of infrastructure and pipeline management.

Our Approach

Trigma implemented the following solution:

Scalable MLOps Framework:
We developed a comprehensive framework using AWS Step Functions, enabling seamless workflow orchestration and direct API integration with AWS services.

Three-Tiered Framework Approach:
– Configuration Deployment Pipeline: Utilizing a ‘model’ Git repository for model configuration and Python scripts, ensuring version control and traceability.
– Pre-processing, Training, and Evaluation Pipeline: A dynamic core pipeline, adaptable to various models, reducing resource duplication and system complexity.
– Model Deployment Pipeline: A decoupled pipeline for flexible model deployment and evaluation, enhancing the approval process and system maintainability.

Empowering Data Scientists:
Our approach allowed data scientists full control over the pipeline processes, enabling them to configure resource requirements independently.

Conclusion
This project with Trigma was not just a technical achievement but a strategic enhancement to our client’s product. It allowed for rapid integration of new ML models without extensive pipeline redesigns, significantly cutting down go-to-market (GTM) time.