MLOps & LLMOps Infrastructure
High-throughput private GPU serving (vLLM, Triton), Kubeflow continuous training pipelines, feature store governance, and sub-second enterprise AI inference on Kubernetes.
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.
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.
End-to-End MLOps Architecture
From raw data ingestion to production model telemetry and drift monitoring.
Ingest & Version
DVC and LakeFS data versioning for total data lineage tracking.
Train & Registry
Kubeflow distributed GPU training runs with MLflow model registry artifacts.
Deploy & Canary
Canary and shadow deployments on KServe for risk-free model evaluation.
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