From Model to Production with Confidence
Get your ML models into production and keep them performing. We build the infrastructure, pipelines, and monitoring systems that turn ML experiments into reliable business capabilities.
MLOps Services
Model Deployment
Deploy models to production with proper containerization, API design, and infrastructure. We support batch, real-time, and edge deployments.
- REST/gRPC API development
- Container orchestration
- Serverless deployment
- Edge/embedded deployment
CI/CD for ML
Automate your ML lifecycle with continuous integration and deployment pipelines designed specifically for machine learning workflows.
- Automated training pipelines
- Model validation gates
- A/B testing frameworks
- Canary deployments
Model Monitoring
Keep your models performing with comprehensive monitoring for drift, performance degradation, and anomalies.
- Data drift detection
- Model performance tracking
- Prediction monitoring
- Alerting & dashboards
Infrastructure Automation
Build reproducible, scalable ML infrastructure using infrastructure as code and cloud-native practices.
- Terraform/Pulumi IaC
- Kubernetes setup
- GPU cluster management
- Cost optimization
Model Registry & Versioning
Manage your models with proper versioning, lineage tracking, and governance throughout their lifecycle.
- Model versioning
- Artifact management
- Model lineage
- Approval workflows
Security & Compliance
Secure your ML systems and ensure compliance with industry regulations and internal policies.
- Access control & authentication
- Audit logging
- Model explainability
- Compliance documentation
The MLOps Maturity Journey
Level 0: Manual Process
Data scientists manually train and deploy models. No automation, no monitoring, no reproducibility. We help you move past this stage quickly.
Level 1: ML Pipeline Automation
Automated training pipelines with proper experiment tracking and model versioning. Manual deployment with basic monitoring.
Level 2: CI/CD for ML
Full CI/CD pipelines for both code and models. Automated testing, validation, and deployment with comprehensive monitoring.
Level 3: Full MLOps
Automated retraining, continuous monitoring, and self-healing systems. Feature stores, A/B testing, and advanced governance.
"Before Fermi Group, deploying a new model took weeks and required heroic effort. Now we deploy multiple models per week with confidence. Their MLOps platform changed everything."
— ML Platform Lead, E-commerce Company
MLOps Technology Stack
ML Platforms
Containers & Orchestration
Monitoring
Infrastructure
Ready to Productionize Your ML?
Let's discuss your deployment challenges and build MLOps infrastructure that scales with your AI ambitions.
Start Your MLOps Journey