Data Science

MLOps — From Experiment to Production at Velocity

Most ML models never make it to production. MLOps industrializes the ML lifecycle with automated pipelines, monitoring, and governance making model delivery predictable and reliable.

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Capabilities

Machine Learning Ops (MLOps) — deep expertise

ML Pipeline Automation

End-to-end pipelines from data ingestion through feature engineering, training, evaluation, and deployment — triggered automatically by data updates.

MLflowKubeflowVertex AI PipelinesSageMaker Pipelines

Feature Store

Centralized feature store providing consistent, versioned definitions shared across models — eliminating training-serving skew and accelerating development.

FeastTectonSageMaker Feature StoreVertex Feature Store

Model Serving Infrastructure

Low-latency model serving using FastAPI, BentoML, TorchServe, and Triton — auto-scaling, A/B traffic splitting, and canary deployment.

FastAPIBentoMLTorchServeTriton Inference Server

Model Monitoring

Drift detection for data, concept, and prediction drift — automated alerting and retraining triggers keeping production models accurate.

EvidentlyArizeWhyLabsAlibi Detect

ML Governance & Explainability

Model cards, lineage tracking, experiment management, and SHAP/LIME explainability — satisfying regulatory requirements for model transparency.

MLflow TrackingSHAPLIMEModel Cards

Experimentation Framework

A/B testing and multi-armed bandit infrastructure for rigorous model comparison — statistical significance and gradual traffic promotion.

A/B TestingMulti-Armed BanditsStatistical TestingShadow Deployment

MLOps Results

ML Production Deployed with Confidence

200+

ML models in production

70%

Reduction in deployment time

95%

Model availability SLA

40%

Faster retraining cycles

Our Approach

From Model Notebook to Production ML Platform

Maturity Assessment

Evaluate your current ML workflow, deployment practices, monitoring gaps, and infrastructure state.

Platform Design

Architect your MLOps stack — experiment tracking, feature store, model registry, and serving layer.

Pipeline Automation

Build CI/CD pipelines for model training, validation gates, and automated deployment workflows.

Governance & Scale

Implement model monitoring, drift detection, audit logs, and a model governance framework.