MLOps Engineering for Production AI
We build robust MLOps pipelines that take your AI models from experimentation to production. Specializing in edge deployment, model optimization, and scalable infrastructure for real-world ML applications.
- Deployment Speed
- 10x
- Faster model deployment
- Cost Reduction
- 70%
- Lower infrastructure costs
- Edge Latency
- <100ms
- Real-time inference
- Uptime
- 99.9%
- Production reliability
About us
Blue Keys Studio is a specialized MLOps engineering consultancy that transforms AI models from research notebooks into production-ready systems. We design and implement robust ML infrastructure, automated pipelines, and deployment strategies that scale.
Our expertise spans the full ML lifecycle—from experiment tracking and model versioning to automated training pipelines, edge optimization, and production monitoring. We combine deep technical knowledge with practical experience to deliver solutions that work in the real world.
Based in Miami, Florida, we partner with companies looking to build production ML systems that are reliable, maintainable, and cost-effective. Whether you're deploying models to the cloud or to edge devices, we help you build the infrastructure and processes to do it right.
- End-to-end MLOps.
- From experimentation to production deployment and monitoring.
- Cloud to edge deployment.
- Expertise in both cloud-scale and edge-optimized ML systems.
- Production excellence.
- Focus on reliability, maintainability, and operational efficiency.
Our services
We specialize in production MLOps engineering, from building robust pipelines to deploying optimized models at the edge. End-to-end solutions for scalable AI systems.
- 01MLOps Pipeline Engineering
Build end-to-end MLOps pipelines from experimentation to production. Automated model training, versioning, validation, and deployment workflows. CI/CD for ML with proper monitoring, drift detection, and model governance.
- 02Edge AI & Model Optimization
Deploy AI models at the edge with optimized inference performance. Model quantization, pruning, and distillation for resource-constrained devices. Real-time prediction capabilities with <100ms latency on edge hardware.
- 03ML Infrastructure & Platform Engineering
Design and implement scalable ML infrastructure using Kubernetes, MLflow, and cloud-native tools. Feature stores, model registries, and experiment tracking. Infrastructure as code for reproducible ML environments.
- 04AI Engineering Consulting
Strategic guidance for AI transformation initiatives. Architecture design, technology selection, and implementation roadmaps. We help you build production-ready AI systems that deliver measurable business value.
MLOps Technology Stack
Modern, production-proven tools and frameworks for building scalable ML systems from training to deployment.
Kubernetes & MLflow
ML Infrastructure
Container orchestration and ML lifecycle management. Model registry, experiment tracking, and deployment automation on cloud-native infrastructure.
PyTorch & TensorFlow
Model Development
Deep learning frameworks for model training and optimization. Model quantization, pruning, and conversion for production deployment.
Airflow & Prefect
Pipeline Orchestration
Workflow automation for data processing and model training. Scheduling, monitoring, and dependency management for complex ML pipelines.
Edge Deployment
TensorFlow Lite & ONNX
Optimized inference on edge devices. Model conversion, quantization, and deployment to IoT devices, mobile, and edge servers.
Our work
We've helped organizations build production ML systems, from implementing robust MLOps pipelines to deploying optimized models at scale.
MLOps Pipeline Implementation
Manual model deployment process taking weeks
Automated CI/CD pipeline with experiment tracking and model registry
10x faster deployment cycle
Edge AI Optimization
Cloud inference costs limiting scalability
Quantized models deployed to edge devices with centralized monitoring
70% cost reduction
ML Infrastructure Platform
Data scientists unable to deploy models independently
Self-service ML platform with feature stores and automated scaling
5+ models deployed per week
Engineering Excellence in MLOps
We combine deep technical expertise in ML infrastructure, deployment automation, and production operations to build AI systems that actually work at scale.
Latest Insights
Stay updated with our latest thoughts on edge AI, open-source models, and MLOps best practices.
Coming Soon
We're working on exciting content about Kubernetes, DevOps, and MLOps. Check back soon for our latest insights!