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Scalable Machine Learning Solutions
Sep 2020–Feb 2021
In the rapidly evolving world of Machine Learning (ML) and Artificial Intelligence (AI), businesses are constantly seeking ways to optimize their ML models for real-world applications. This case study delves into our work with a B2B SaaS product company, focusing on real-time price optimization through ML models. Our objective was to develop a scalable and secure MLOps framework to accommodate the growing needs of ML in their product offerings.
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.
