Enhancing Machine Learning Operations (MLOps) on Mesosphere

Goals

To implement an MLOps framework on Mesosphere for a leading financial firm, enhancing deployment, management, and monitoring of machine learning models. The aim was to maximize data value and refine decision-making processes.

Challenges

The firm utilized machine learning for business insights but faced challenges in model deployment, management, and monitoring. They approached Trigma for a comprehensive MLOps solution on Mesosphere.

Our Approach

Trigma developed an MLOps framework encompassing:

Automated Deployment: Streamlining model deployment to Mesosphere, reducing manual intervention.

Model Management: Implementing version control, performance tracking, and A/B testing for optimal model performance.

Monitoring and Logging: Integrating tools for continuous model performance tracking and issue identification.

Model Validation and Retraining: Ensuring model accuracy and relevance through regular validation and retraining.

Collaboration and Governance: Establishing collaborative tools and compliance with data governance policies.

Results

The MLOps framework delivered:

Streamlined Deployment: Quicker model production deployment, enhancing operational efficiency.

Improved Model Management: Better model performance and risk management.

Enhanced Monitoring: Insightful model performance tracking, leading to more effective issue resolution.

Ongoing Model Validation and Retraining: Continued relevance and accuracy of machine learning models.

Better Collaboration and Governance: Improved team synergy and adherence to data governance.