Proposal: create live Deep Learning service for analyzing test output

Thanks to @LongyuZhang , we have the initial Deep Learning (DL) prototype that takes test outputs (from TRSS) as the training data to predict possible issues. The prototype uses Tensorflow for test output classification. It is improved with TF-IDF method and weighted model. We have achieved a lot so far. However, there are lots of work that need to be done. For example, we need to further refine the model, collect more types of test outputs data, utilize more detailed information for DL model training and testing. Our goal is to refine the DL model and use it to suggest possible issues/solutions related to the test failure.

Currently, the work has mostly done locally. It is very time consuming, limited data set, and unreliable. It will be great if we can create a live DL service using a machine that can run machine learning so that we can
- have API to get the result from DL model at runtime
- constantly using new TRSS data for model training and refinement
- get feedback and adjustment quickly to shorten the development cycle


This can be separated into two parts:
1. create the API that uses the trained model to predict possible issues
2. automate data gathering and DL training process to generate trained model

For part 2, we would like to get a server with GPU that can run machine learning
https://www.tensorflow.org/install/gpu

We should also investigate the existing machine learning pipelines. For example https://cloud.google.com/blog/products/ai-machine-learning/cloud-ai-helps-you-train-and-serve-tensorflow-tfx-pipelines-seamlessly-and-at-scale

Start by locating the existing TensorFlow prototype and how it consumes TRSS test output data. Define the API and automated data-gathering and training boundaries, then verify that a live service can predict possible issues and retrain from new data.

Written by the indexing model from the issue text.