Developers often misattribute performance issues to models when the real problem lies in pre/post-processing code. Build a pluggable benchmarking system that separately measures model inference versus harness overhead.
Target ML engineers who need to optimize production deployments. Offer visualization of time spent in each pipeline stage.
Sell as a SaaS product with pay-per-test pricing or enterprise licenses.
MVP could be a Python library that wraps existing benchmarking tools with standardized instrumentation.
Risk is competition from open-source alternatives, so focus on seamless integration with major ML frameworks.