End-to-End MLOps Infrastructure

Situation: Tribal's data science team was operating in an experimental phase with promising model prototypes and research results, but lacked any structured process or infrastructure to move these models from development environments into production. The team had valuable ML insights but no pathway to deliver actual business value, creating a significant gap between data science experimentation and operational impact.

Task: Bridge the critical gap between model development and production deployment by designing and implementing a comprehensive MLOps infrastructure that would enable the team to transition from pure experimentation to delivering production-ready ML solutions with reliable, scalable deployment processes across multiple business units.

Action:

Architected CI/CD pipelines using GitLab CI/CD with automated testing frameworks for ML models, including data validation, model performance checks, and integration tests

Implemented containerization using Docker for consistent environments and Kubernetes for orchestration and scaling across AWS EKS

Built ML orchestration workflows using Dagster to manage complex data and model pipelines with dependency tracking and automatic retry mechanisms

Developed monitoring and alerting systems using AWS CloudWatch and custom dashboards to track model performance, data drift, and infrastructure health

Created automated deployment processes with blue-green deployment strategies and automated rollback capabilities for failed deployments

Established model versioning and artifact management using MLflow and AWS S3 for reproducible experiments and model lineage tracking

Built self-service APIs and web interfaces to enable easy model access and reduce friction for internal teams

Collaborated closely with data scientists to understand specific use case requirements and deploy tailored ML solutions for the support team, legal team, and other business units, ensuring each deployment met departmental needs and workflows

Result: Successfully transformed the team from pure experimentation to production-ready ML delivery across multiple business functions. Established the foundation that enabled various ML use cases to move from prototype to production for the first time in the company's history, serving internal teams including support, legal, and other departments. The infrastructure provided reliable deployment pathways and monitoring capabilities that gave stakeholders confidence in ML-driven solutions. This transformation marked a pivotal shift for the organization, establishing data science as a core business capability rather than just a research function, with tangible impact across different business units.