Your ML Models Deserve Production-Grade Infrastructure
From Jupyter notebooks to production pipelines. We build the Kubeflow, MLflow, and GPU infrastructure that gets your models serving real traffic.
Why Teams Choose Us for MLOps
GPU Infrastructure Experts
We configure NVIDIA GPU clusters, optimize CUDA workloads, and right-size your compute so you stop burning money on idle resources.
Kubeflow + MLflow Pipelines
Not just setup, but production-hardened pipelines with experiment tracking, model versioning, and automated retraining triggers.
Drift Detection + Monitoring
Real-time model performance monitoring, data drift alerts, and automated rollback so your predictions stay accurate in production.
Our Services
MLOps Pipeline Automation
Design and implement CI/CD pipelines specifically for machine learning workflows. Expertise in Kubeflow Pipelines and MLflow tracking for reproducibility and scalability.
Model Deployment & Management
Deploy models with real-time monitoring and continuous integration. Support for hybrid setups, from Azure MLOps to AWS SageMaker.
Infrastructure Management
GPU-optimized workloads for high-performance AI training and inference. Manage on-premise data centers with GPU clusters, hybrid cloud, and public cloud environments.
Monitoring & Optimization
Real-time drift detection, error analysis, and predictive insights for your deployed models. Ensure peak performance and reliability of your AI systems.
Data Engineering for AI
Production ETL pipelines using Apache Airflow, Spark, and Kafka. Ensure your data is clean, accessible, and ready for machine learning workflows.
Featured Projects
GPU-Optimized Model Deployment for Healthcare
Deployed a real-time patient monitoring system using GPU-powered infrastructure. Reduced latency by 40% and achieved 24/7 availability with Kubernetes orchestration.
Key Achievements
- 40% reduction in inference latency
- 99.99% uptime achieved
- Scalable to handle 10,000+ concurrent patients
Hybrid Cloud MLOps for Retail
Designed a scalable MLflow tracking system across on-premise and cloud environments. Enabled continuous CI/CD with 99.9% uptime for model training and deployment.
Technical Details
- 20-node GPU cluster for distributed training
- 30% reduction in compute costs
- Automated A/B testing pipeline for continuous improvement
FAQ
MLOps Questions, Answered
What is MLOps and why does it matter?
MLOps is the practice of running machine learning systems in production reliably. It covers training pipeline orchestration, experiment tracking, model registry, serving infrastructure, and monitoring. Without it, models stay in Jupyter notebooks and never reach customers. With it, models ship as repeatable, monitored, versioned production services.
What MLOps tools do you use in production?
Kubeflow Pipelines for orchestration, MLflow for experiment tracking and model registry, KServe for inference serving, ArgoCD for GitOps deployment, and Prometheus + Grafana with NVIDIA DCGM Exporter for GPU and model monitoring. We pair these with NVIDIA GPU Operator, AMP mixed-precision training, and Karpenter for cloud burst.
How long does it take to go from prototype to production ML?
For a focused model with clean data, 4 to 8 weeks. The phases are: 2 weeks for Kubeflow and MLflow infrastructure setup, 2 weeks for pipeline implementation and CI/CD, 1 to 2 weeks for KServe inference deployment and monitoring, and a final 1 to 2 weeks for load testing and on-call enablement.
Can you work on-premises or hybrid for ML workloads?
Yes. We deploy Kubeflow on bare-metal Kubernetes for clients with data residency requirements. Common in healthcare, finance, and MENA-based clients. We support NVIDIA GPU operator with MIG partitioning, VMware Bitfusion, and Proxmox GPU passthrough on on-prem hardware, with optional cloud burst via Karpenter.
Do you handle GPU optimization too?
Yes. Most teams running GPU workloads see 30 to 40% utilization while assuming they are CPU-bound. We tune data loaders, enable mixed precision, configure topology-aware scheduling on multi-GPU jobs, set up MIG partitioning for inference, and right-size GPU type to workload (A100 for training, L4 for inference).
How much does an MLOps engagement cost?
Pricing follows our two standard engagement patterns: Managed Engineering Pod from $10,000/m for a full delivery team (architect + engineers + PM) or Embedded Senior DevOps from $2,500/m for a senior engineer placement backed by our broader team. We start most MLOps clients with a free MLOps audit call to scope which pattern fits.
Ship your ML to production
Most MLOps engagements run as one of two patterns. Both are scoped during a free 30-minute discovery call, with a free MLOps audit included.
Pattern A
Managed Engineering Pod
Full delivery team from $10,000/m
Pattern B
Embedded Senior DevOps
Senior engineer from $2,500/m
See full pricing patterns.