You have the model architecture, but getting clean, continuous, and representative training data is slowing you down. I design and build production-grade ingestion systems, synthetic data engines, and human-in-the-loop annotation workflows.
Every stage feeds the next — human corrections flow straight back into training.
The problem
Critical failure modes are hard to capture in the wild — leaving your model blind exactly where it matters most.
Ad-hoc collection breaks at scale, yielding noisy, corrupted, or unrepresentative inputs.
Human corrections stay stranded in spreadsheets instead of powering active learning and retraining.
Core services
Programmatic and generative pipelines to bootstrap photorealistic, pre-annotated datasets for rare edge cases.
Resilient event capture, validation, and real-time cleaning for production-scale data.
Custom annotation interfaces and databases that automatically route low-confidence inferences to human reviewers for continuous retraining.
Selected case studies
Built programmatic data generators outputting photorealistic, pre-annotated visual samples across hundreds of simulated lighting and occlusion scenarios.
Developed a centralized active learning pipeline pairing model inferences with rapid human verification and direct structured storage for retraining.
Engineered lightweight, zero-data-loss telemetry software capturing high-concurrency event streams across hotel platforms.
About
“Software engineering discipline meets rigorous data science.”
Leon Faber works at the seam between software engineering and statistical data analysis. He designs and builds the production-grade engineering scaffolding AI teams need — ingestion, validation, synthesis, annotation — so researchers can focus on modeling instead of debugging pipeline plumbing.
Working with VPs of Engineering, Heads of AI, and technical founders
Contact & intake
Tell me where the flywheel is stalling — data volume, modality, and what you have tried so far. I will come back with a concrete read on scope and approach.
INFO@INNODYN.NL
