Services · MLOps & Model Serving
Training a model is 20% of the job. We build the other 80% — feature stores, serving infra, drift monitors, and retraining pipelines that keep your models reliable at scale.
Architecture
From raw feature engineering through live serving and automated retraining — a closed loop that keeps models fresh without manual intervention.
Our Approach
We design point-in-time correct feature pipelines using Feast or Tecton, preventing training-serving skew from day one.
Every training run is tracked in MLflow or W&B — parameters, metrics, dataset hash, model artifact. Re-running any experiment produces identical results.
KServe or SageMaker endpoints with auto-scaling, request batching, and GPU memory optimizations tuned to your latency SLA.
Evidently monitors data and concept drift. Alerts wire to Airflow retrain DAGs — models self-heal before users notice degradation.
What We Solved
Batch fraud scoring ran nightly — fraudulent transactions weren't caught until the next day. Dispute costs were $4M+ annually.
Built a real-time inference pipeline on KServe with XGBoost and a GNN fraud graph. Feature store on Redis for <1ms feature lookup. Model updated weekly via MLflow-tracked retraining DAG.
A static demand forecast retrained quarterly couldn't adapt to promotions, seasonality shifts, or supply chain disruptions — leading to $18M in annual overstock.
SageMaker pipeline with Prophet + LightGBM ensemble, Evidently drift monitoring on feature distributions, and weekly automated retraining triggered by drift thresholds.
14 different teams each had their own ad-hoc training environment — Jupyter notebooks, bare EC2 instances, no reproducibility, no shared feature pipelines.
Deployed Kubeflow on EKS with a shared Feast feature store, MLflow Model Registry, and Argo Workflows for pipeline orchestration. RBAC per team, shared GPU node pools.
Technologies We Deploy
Tell us your use case — we'll scope a production-grade MLOps stack in one call.