We address limitations in manifold-learning approaches by proposing atlas-based methods that enable direct machine learning on latent manifolds. We develop a data structure for maintaining differentiable atlases supporting Riemannian optimization, plus an unsupervised learning heuristic for point cloud data. Testing demonstrates advantages in efficiency and accuracy in selected settings, with improved interpretability shown through Klein bottle classification and RNA velocity analysis.