Machine Learning frameworks undeniably went through a mind-blowing evolution over the last 4 years. The multiple iterations created various trends, one of which has put a bright spotlight on the importance of the framework’s interface. Developers want the flexibility and transparency of low-level APIs in their work with high-level APIs. TensorFlow 2.0, Keras and PyTorch are making steps to solve this nontrivial puzzle, however wrapping the low-level APIs makes it tedious to write boilerplate code for visualizations of different variables during runtime. We are introducing a unique flexible ML platform that gives developers full transparency into the process of machine learning models’ development, and enables fast model iteration.