  Machine learning and deep learning-based decision making has become part of
today's software. The goal of this work is to ensure that machine learning and
deep learning-based systems are as trusted as traditional software. Traditional
software is made dependable by following rigorous practice like static
analysis, testing, debugging, verifying, and repairing throughout the
development and maintenance life-cycle. Similarly for machine learning systems,
we need to keep these models up to date so that their performance is not
compromised. For this, current systems rely on scheduled re-training of these
models as new data kicks in. In this work, we propose to measure the data drift
that takes place when new data kicks in so that one can adaptively re-train the
models whenever re-training is actually required irrespective of schedules. In
addition to that, we generate various explanations at sentence level and
dataset level to capture why a given payload text has drifted.
