  In this work we present an "out-of-the-box" application of Machine Learning
(ML) optimizers for an industrial optimization problem. We introduce a
piecewise polynomial model (spline) for fitting of $\mathcal{C}^k$-continuos
functions, which can be deployed in a cam approximation setting. We then use
the gradient descent optimization context provided by the machine learning
framework TensorFlow to optimize the model parameters with respect to
approximation quality and $\mathcal{C}^k$-continuity and evaluate available
optimizers. Our experiments show that the problem solution is feasible using
TensorFlow gradient tapes and that AMSGrad and SGD show the best results among
available TensorFlow optimizers. Furthermore, we introduce a novel
regularization approach to improve SGD convergence. Although experiments show
that remaining discontinuities after optimization are small, we can eliminate
these errors using a presented algorithm which has impact only on affected
derivatives in the local spline segment.
