MLOps Platform for Global Energy Company

Built end-to-end MLOps infrastructure enabling rapid deployment of predictive maintenance models across 50+ facilities.

Energy & Utilities

2024

8 months

10 engineers

90% faster

Deployment Time

From model training to production

25+ models

Models in Production

Deployed across facilities

$2M+ saved

Downtime Prevented

Through predictive maintenance

3x increase

Data Scientist Productivity

Time spent on actual ML work

The Challenge

The energy company needed to scale their AI initiatives across operations: • Data scientists spending 70% of time on infrastructure vs. modeling • No standardized way to deploy models to production • Models in production with no monitoring or versioning • Inconsistent environments between development and production • Difficulty replicating experiments and results

Our Solution

We implemented a comprehensive MLOps platform: **ML Pipeline Infrastructure** • Deployed Kubeflow for orchestrated ML workflows • Built feature store for consistent feature engineering • Created model registry with versioning and lineage **Automated Training & Deployment** • CI/CD pipelines for model training and deployment • A/B testing framework for model comparison • Automated rollback capabilities **Monitoring & Observability** • Real-time model performance dashboards • Data drift detection and alerting • Prediction logging and analysis **Self-Service Platform** • Web interface for data scientists to deploy models • Template-based project creation • Integrated experiment tracking

"The MLOps platform has been a game-changer. Our data scientists can now focus on solving problems instead of fighting infrastructure. We've deployed more models in 6 months than in the previous 3 years."

Head of AI & Analytics

International Energy Company