In data management, indices are used to retrieve data in faster than linear complexity. A representative of such index could be B-tree, which is used for classical, attribute data; to handle more complex data such as high-dimensional descriptors extracted from raw images or videos where no attributes are used to store or retrieve the data, index structures such as M-tree and M-index have been developed. Recently, a paper called The Case for Learned Index Structures has been published, arguing for benefits of replacing the standard index structures for attribute data with deep-learning models. The subject of this thesis is to implement a Learned Index Structure for selected Matric Spaces Indexes (M-tree and M-index) and dataset (either CoPhIR or Profiset) in Python, utilizing the Tensorflow and scikit-learn.