Many machine learning practitioners struggle to implement optimization algorithms in PyTorch due to sparse documentation.
Build a series of Jupyter notebooks or a lightweight library showing how to solve problems like the 0/1 Knapsack efficiently using PyTorch's autograd and tensor operations.
ML teams at mid-size companies would pay for well-documented, production-ready implementations to accelerate their prototyping phases.
Start with a single notebook walking through the Knapsack solution, then expand to related problems.
The risk is that most practitioners only need mainstream algorithms, making the audience niche.