Try various container operating systems & kubernetes distributions and compare them to SUSE MicroOS and CaaSP, respectively.

In lieu of official hosting which was requested a year ago, go ahead and refactor approach to work on AWS S3. The previous approaches used either soft or hard links to avoid duplicating files that remain unchanged between snapshots, but S3 is not a file-system and does not support links. The closest comparable concept are object level redirects, but managing and creating those would be combersome, slow, and result in a higher bill. To avoid that an alternative that does not require object level redirects to simulate links, but that does not requiring duplicating files would be preferable. In addition the tool built to provide such snapshots on S3 should be easily deployable and setup to run automatically on AWS.

Planning to do some deep learning course sessions e.g. fastai , google ML crash course etc. Also try to understand common tools (tensorflow, jupyter notebook, numpy, pandas, pytorch) and practices e.g. Convoluation neutral nets, SGD used to solve learning problems. Aim is to get ready for kaggle competition (https://www.kaggle.com/competitions) eventually to test out learning and develop intuition around categories of learning problems.

About 3 years ago I wrote some Python and Kivy code for my Samsung Galaxy S4 in QPython. Unfortunately, QPython updated and broke its kivy compatibility. I've been waiting for them to fix it, but mostly have given up. So instead, just investigate how to turn the kivy code into an .apk for installation on an Android device! There are directions for this on the kivy website.