Scalability Issues
Difficulty in scaling ML models from development to production
ML infrastructure for training and serving your models
Move models from notebook to production on infrastructure we run for our own products.
Talk to a founder →Related things we’ve built.
- Echo Realms — we scaled inference to generate huge amounts of interactive content.
- NTC Slider Factory — the training pipeline behind 1,000+ LoRAs and DoRAs.
Common ML Infrastructure Challenges
Data Management Complexity
Struggle with handling large-scale data for ML training and inference
Deployment Bottlenecks
Slow and error-prone processes for deploying models to production
Monitoring and Maintenance
Lack of robust systems for monitoring model performance and drift
A scope we can shape together.
These are starting points for a conversation. We’ll agree the scope and timing around your project.
For AI-First Startups
- Scalable ML Platform Setup
- Custom MLOps Pipeline Development
- Cloud-Native ML Infrastructure
Typical Timeline: 6-10 weeks
For Large Enterprises
- Enterprise-Wide ML Platform
- On-Premise to Cloud Migration
- Multi-Cloud ML Strategy
Typical Timeline: 12-20 weeks
For Research Institutions
- High-Performance Computing Clusters
- Distributed Training Infrastructure
- Large-Scale Data Processing Pipelines
Typical Timeline: 8-16 weeks
Technologies we use.
The problem decides the approach. These are some of the tools we reach for in this kind of work.
cloud_platforms
- AWS
- Google Cloud
- Azure
ml_tools
- Kubernetes
- Docker
- Kubeflow
- MLflow
- Airflow
languages
- Python
- Go
- Scala
Talk to us about your ML infrastructure
Email a few lines about what you’re building, what you’d like to improve, or where you’re stuck. Martyn or Mikkel will reply personally.
Or reach a founder directly: Martyn Garcia · Mikkel Garcia