  Continuous machine learning pipelines are common in industrial settings where
models are periodically trained on data streams. Unfortunately, concept drifts
may occur in data streams where the joint distribution of the data X and label
y, P(X, y), changes over time and possibly degrade model accuracy. Existing
concept drift adaptation approaches mostly focus on updating the model to the
new data possibly using ensemble techniques of previous models and tend to
discard the drifted historical data. However, we contend that explicitly
utilizing the drifted data together leads to much better model accuracy and
propose Quilt, a data-centric framework for identifying and selecting data
segments that maximize model accuracy. To address the potential downside of
efficiency, Quilt extends existing data subset selection techniques, which can
be used to reduce the training data without compromising model accuracy. These
techniques cannot be used as is because they only assume virtual drifts where
the posterior probabilities P(y|X) are assumed not to change. In contrast, a
key challenge in our setup is to also discard undesirable data segments with
concept drifts. Quilt thus discards drifted data segments and selects data
segment subsets holistically for accurate and efficient model training. The two
operations use gradient-based scores, which have little computation overhead.
In our experiments, we show that Quilt outperforms state-of-the-art drift
adaptation and data selection baselines on synthetic and real datasets.
