This example doesn't do the entire numba project justice, but if you've ever written a for-loop in a bit of python code that does number crunching, you'll notice how much it slows everything down, and the numba jit provides a decorator that yields an extremely quick win to get often 1-2 orders of magnitude of improvement in calculation time. It's less of a win if you're only every working with already vectorised data structures and algorithms.

I was confused by your comment though, specifically the idea that you are using tensorflow because you 'mostly work with simple data that doesn't require complicated calculations'. This seems very contradictory. Have I misunderstood?