ML Pipeline · MLOps Platform
End-to-end MLOps platform with automated training pipelines, model versioning, A/B testing, and seamless production deployment.
Case study
ML Pipeline · MLOps Platform
End-to-end MLOps platform with automated training pipelines, model versioning, A/B testing, and seamless production deployment.
End-to-end MLOps platform with automated training, model versioning, A/B testing, and production deployment pipelines.
The Challenge
The team needed an MLOps platform to manage model training, versioning, and deployment but existing solutions were too complex or didn't fit their workflow.
The Solution
Delivered an end-to-end MLOps platform with automated training pipelines, model versioning, A/B testing capabilities, and seamless production deployment. The system now manages hundreds of models with automated monitoring and rollback capabilities.
Key results
Models trained and deployed
Production deployments
Platform reliability
Stack
Timeline
Week 1–3
MLOps architecture design, tool selection, and infrastructure planning
Week 4–8
Pipeline development, model registry, and CI/CD integration
Week 9–10
A/B testing framework, monitoring, and team training
“The MLOps platform reduced our model deployment time from weeks to hours.”
Project Details
Technical implementation and architecture overview
Training pipelines
Automated ML training pipelines with hyperparameter tuning, experiment tracking, and model versioning.
CI/CD for ML
Built CI/CD pipelines for model deployment with automated testing, validation, and rollback capabilities.
Model monitoring
Real-time model performance monitoring, drift detection, and automated alerts for production models.
FAQs
What do you build? +
Web3, AI, Systems, Web. End-to-end. One person. From idea to deployed.
Do you do consultancy? +
Yes. Architecture, stack selection, code reviews. Hourly or contract. Get unstuck fast.
How fast can you deliver? +
Fast. I focus on going live. Less bureaucracy, more shipping. Let's discuss timeline.
One person for everything? +
Yes. Frontend, backend, infrastructure, deployment. Complete systems. End-to-end.