Xi Huang

Monitoring: Sentry and LogDNA are tools we found useful for monitoring our webapp after deployment. Sentry is widely used for error-tracking. Its filtering and GUI allows us to easily look at not only the errors but also the overall performances. LogDNA similarly processes logs in an organized manner. In addition, both the tools are easily integratable with our deployment platform Heroku.

Docker provides environment management, beneficial to the speed and efficiency of reproduction. It allows scalability and consistency. To our team's benefit, Docker provides a safe and isolated space for local development.

We changed to Python instead of Java to have the back-end processing in the same language as our data analysis module. In addition, Python has a lot of libraries for data-processing. We intend to use Flask for our back-end web development. Flask is a simple, straight-forward framework for our purposes. Flask also has a large community which is beneficial to the development process.

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.