Tensorflow on MapR Tutorial: a Perfect Place to Start

Even if you haven't had a chance to check out TensorFlow in detail, it's clear that your choice of platform has a big impact just as it does for other machine learning frameworks. The adventure from trial to production involves many intermediate destinations, from feature engineering to model-building to execution and real-time evaluation. Even a model with the most spectacular F1-score is only as good as how effectively you can put it to use helping customers. Questions arise such as: do you need to evaluate against data for offline or online analysis (or both)? Where does the preprocessed (or feature-engineered) data live on its way to TensorFlow? Is there a way to preserve data lineage as it moves through the various stages to support both security concerns as well as easy debugging?