MLOps & Infrastructure
MLOps & Infrastructure
We implement the infrastructure and processes needed to deploy ML models reliably, monitor their performance, and retrain them automatically. Turn your notebooks into production systems.
Cloud infrastructure setup (AWS/GCP)
CI/CD pipelines for ML (automated testing & deployment)
Model versioning & experiment tracking (MLflow, Weights & Biases)
Automated retraining pipelines
Model serving & scaling (handle millions of predictions)
What We Do:
- Cloud infrastructure setup (AWS/GCP)
- CI/CD pipelines for ML (automated testing & deployment)
- Model versioning & experiment tracking (MLflow, Weights & Biases)
- Automated retraining pipelines
- Model serving & scaling (handle millions of predictions)
- A/B testing frameworks (compare model versions)
- Monitoring & drift detection (data drift, concept drift)
- Alerting & incident response (PagerDuty, Slack)
- Infrastructure as Code (Terraform, CloudFormation)
- Feature stores (centralized feature management)
TECH STACK
AWS (SageMaker, Lambda, ECS), GCP (Vertex AI, Cloud Run), MLflow, Kubeflow, GitHub Actions, Terraform, Docker, Kubernetes
TIMELINE
3-8 weeks
INVESTMENT
From $15,000
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