MLOps & Production AI Pipelines

MLOps Infrastructure Engineering

Automate dataset preparation, feature store management, model training, evaluation, and sub-second inference serving on Kubernetes.

Consult MLOps Engineers

PRODUCTION SCALE

Continuous AI Model Delivery with Zero Downtime

CogFocus™ builds resilient MLOps pipelines that bridge experimental data science and high-availability enterprise production. We automate continuous integration and deployment for AI models (CT/CD), monitor model drift in real time, and scale inference workloads dynamically on AWS EKS, Azure AKS, or private Kubernetes clusters.

Kubeflow Pipelines Feast & Hopsworks Feature Store Triton & vLLM Inference MLflow Experiment Tracking

MLOps Platform Pillars

1. Automated Data & Feature Pipelines

Version-controlled data lineage, automated ETL transformations, and centralized feature store indexing.

2. Continuous Model Training & Retraining

Triggered model retraining upon data drift detection, automated hyperparameter tuning, and CI/CD model registries.

3. High-Throughput Inference Serving

Sub-millisecond model serving using Triton, Ray Serve, and vLLM with GPU auto-scaling on Kubernetes.

MAPPING THE LIFECYCLE

End-to-End MLOps Architecture

From raw data ingestion to production model telemetry and drift monitoring.

PHASE 01

Ingest & Version

DVC and LakeFS data versioning for total data lineage tracking.

PHASE 02

Train & Registry

Kubeflow distributed GPU training runs with MLflow model registry artifacts.

PHASE 03

Deploy & Canary

Canary and shadow deployments on KServe for risk-free model evaluation.

PHASE 04

Drift Telemetry

Evidently AI real-time data drift monitoring with automated retraining triggers.

Build Industrial MLOps Pipelines

Partner with CogFocus™ solution architects to deploy production MLOps inside your cloud infrastructure.

Schedule MLOps Consultation