MLOps & LLMOps Platform for an Enterprise Data Team

Enterprise Technology: Dozens of ML models and new LLM apps were deployed by hand, with no monitoring, versioning or evaluation before release. Deployment…

Client: Enterprise Technology Company. Industry: Enterprise Technology.

Challenge: Dozens of ML models and new LLM apps were deployed by hand, with no monitoring, versioning or evaluation before release.

Solution: Set up a shared platform — model registry, CI/CD for models, drift monitoring, LLM tracing, evaluation suites and token-cost dashboards — with templates teams reuse for every new model.

Technologies: MLflow, Kubeflow, Evidently, Langfuse, LiteLLM, GitHub Actions.

• Deployment Time: Weeks → hours
• Models Managed: 60+
• Incidents: -50%
• LLM Cost Visibility: 100%

• All case studies