Your models are only as good as the infrastructure behind them. QuantPi builds production-grade MLOps platforms — automated training, model versioning, drift detection, A/B testing, and auto-scaling — so your AI stays accurate, efficient, and reliable at any scale.
Most ML teams spend 80% of their time on infrastructure, not innovation. Models that worked in notebooks fail in production. Retraining is manual. Monitoring is an afterthought. Data drift goes undetected until a customer complains. QuantPi solves this by engineering ML platforms that automate the entire lifecycle — from data ingestion to model serving.
We build on battle-tested tools like MLflow, Kubeflow, Airflow, and Prometheus, and we deploy on AWS, GCP, or Azure. Every pipeline includes automated data validation, experiment tracking, model versioning, performance monitoring, drift detection, and auto-retraining triggers. Your models improve continuously without manual intervention.
Schedule a DemoEnd-to-end training orchestration with hyperparameter tuning, cross-validation, and early stopping. Triggered by schedule, data changes, or performance degradation.
Central model repository with lineage tracking, metadata, and promotion workflows. Every model is reproducible, auditable, and rollback-ready.
Real-time statistical monitoring for feature drift, concept drift, and data quality degradation. Alerts fire before accuracy drops.
Gradual traffic shifting between model versions with automated rollback. Measure real-world impact before full deployment.
Custom dashboards for latency, throughput, accuracy, and business KPIs. Integrated with PagerDuty, Slack, and Opsgenie.
GPU and CPU endpoint scaling based on traffic patterns. Spot instance optimization to slash inference costs by up to 70%.
We assess your current ML stack, identify bottlenecks, and define the target architecture and migration plan.
1 weekDesign and implement training pipelines, feature stores, model registry, and CI/CD for ML.
3-5 weeksDeploy drift detection, alerting, auto-retraining, and performance dashboards.
2-3 weeksCost optimization, documentation, team training, and ongoing support setup.
1-2 weeksEliminate manual retraining with pipelines that detect performance degradation and automatically retrain, validate, and deploy updated models.
Learn moreBuild streaming feature pipelines that compute and serve features in real-time for fraud detection, recommendation, and personalization systems.
Learn moreManage ensemble models, champion-challenger setups, and model cascades with centralized orchestration and unified monitoring.
Learn moreReduce inference costs by 50-70% through model quantization, batching strategies, spot instances, and intelligent auto-scaling.
Learn moreWe are tool-agnostic but commonly build on MLflow, Kubeflow, Airflow, Feast, and Seldon. We choose the best tools for your stack and scale.
Absolutely. Most engagements involve integrating with existing data warehouses, CI/CD pipelines, and cloud accounts rather than ripping and replacing.
We deploy statistical tests (KS test, PSI, JS divergence) on feature distributions and model predictions. Alerts trigger automated retraining when thresholds breach.
AWS SageMaker, GCP Vertex AI, Azure ML, and self-managed Kubernetes. We optimize for your preferred cloud provider.
Start with a technical conversation. No pitch decks, no pressure — just a discussion about what’s possible.