DingAI · Museums
Artifact Recognition Platform
2023 · Founding ML Engineer · Co-founder
An AI image classification platform identifying museum artifacts, built with two co-founders and external partners.
Composition
- InferenceDocker · AWS Lambda
- Service layerFlask · FastAPI
- ConcurrencyPython multithreading
- PersistenceManaged database
- ValidationStress + reliability testing
Mission & Constraints
Co-led product direction and system architecture — roadmap and deployment strategy alongside the build, not after it.
The pipeline was containerised and serverless from the start, which is where the 90 % operational cost reduction came from; portability was the design goal and the cost was the consequence.
Performance work was measured, not assumed: multithreaded processing and stress-tested tuning in Python produced a 94 % speed improvement, with the bottlenecks found by load testing rather than by inspection.
Implementation
Inference
Docker · AWS Lambda
Service layer
Flask · FastAPI
Concurrency
Python multithreading
Persistence
Managed database
Validation
Stress + reliability testing
Measured Outcomes
Operational infrastructure cost
−90 %baseline toresidual
DingAI — serverless AI image classification
Inference pipeline speed
+94 %baseline toachieved
DingAI — multithreaded processing, stress-tested in Python
Questions about this work?
Happy to go deeper on the architecture, the constraints, or how the numbers were measured.