The Infrastructure Challenge
When we started deploying ML models at scale across Africa, we quickly realized that the infrastructure patterns from Silicon Valley don't translate directly. Here's what we learned.
Connectivity Realities
The Numbers
Our Approach
We designed our infrastructure for the worst case:
Hardware Constraints
Device Diversity
Our models need to run on:
Optimization Techniques
We use multiple techniques to make this work:
Model Quantization
We convert 32-bit floating point models to 8-bit integers, reducing size by 4x with minimal accuracy loss.
Knowledge Distillation
Large models "teach" smaller models, allowing us to deploy lightweight versions that capture 90%+ of the capability.
Architecture Search
We use neural architecture search to find model designs that maximize accuracy per FLOP.
Deployment Patterns
Blue-Green Deployments
We maintain two production environments and switch traffic between them. This allows zero-downtime deployments and instant rollback if issues arise.
Canary Releases
New model versions first serve 1% of traffic, gradually increasing if metrics look good. This catches issues before they affect all users.
Regional Sharding
We deploy models to regional data centers across Africa—Nairobi, Lagos, Cape Town, Cairo. Users connect to the nearest one, reducing latency.
Monitoring and Observability
Key Metrics We Track
Automated Alerts
We alert on:
Lessons for Others
The infrastructure challenges are real, but they're solvable. The result is AI systems that work reliably for users regardless of their connectivity or device.