Take Your Models From
Notebook to Production
Most ML models never make it to production — or fail silently when they do. ZippyOPS builds the automation layer that connects your data science work to reliable, monitored, production-grade model deployments.
What We Do
We implement end-to-end ML pipeline automation — from experiment tracking and feature stores to automated retraining, model serving and drift monitoring — so your models keep working long after the first deployment.
- ML pipeline automation — training, validation, versioning and packaging with Kubeflow and Airflow
- Experiment tracking with MLflow and Weights & Biases
- Feature store design and implementation with Feast
- Model serving with Seldon, BentoML, vLLM and TorchServe for high-throughput inference
- Model drift monitoring and automated retraining trigger pipelines
- CI/CD for ML — automated model evaluation gates before promotion to production
- Model governance — lineage, explainability and audit trail for regulated industries
What You'll Walk Away With
An end-to-end ML pipeline — from feature engineering to production serving — fully automated
Experiment tracking giving your data science team reproducible, comparable results
Model drift detection alerting your team before degraded models impact your users
CI/CD for ML with automated evaluation gates ensuring only quality models reach production
Real Projects. Real Results.
View All Projects →Ready to Take ML to Production?
Book a free MLOps assessment. We'll review your current model lifecycle and design a production-grade pipeline for your team.