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entail. Also, multiple models may work
on multiple data representations (like
different granularities of aggregations);
hence the ability to have many models
being evaluated in parallel is necessary.
An evolving conceptual tool for
such parallel evaluation is ensemble
modelling – choosing a combination
of outputs from multiple models.
We anticipate more focus in the data
science community on building efficient,
automated ensemble models to deal
with infinite data sets.
We advise the data scientist to
prepare to apply techniques
from key areas that will direct
the evolution of data sciences
for infinite data sets such as
ensemble models, AutoML and
concept drift.
The data scientist must also prepare for
adapting to many variances in data
representation and data set properties
over the lifetime of the model. Thus
the need for data cleansing and
data imputation must be sensed
and addressed with time and quality
guarantees; as should the need for
revising approximations in response to
property changes. For hand-crafted (non- handling of infinite data sets and the
AI) models, supervision (semi- or full) for different challenges that infinite data
training is inevitable; but preparation for sets pose, like variations over time in
the aforementioned variances makes its
automation critical. AutoML engineered
machine learning pipelines will progress
to solve these challenges. Data scientists key areas that will direct the evolution
must keep track of developments in
this nascent field and adopt state-
of-the-art algorithms for automated
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feature extraction, construction and
transformation, automated handling of
skewed and missing data, and automated
post-processing and calibration of target
values.
While handling the expected
heterogeneity and non-stationarity in
the distributions in the data set and the
changes in the data representation; the
data scientist should also be able to make
new conclusions basis new correlations.
In our parlance this means that evolving
approximation functions map new
information in the target variables. This
variation in the target variable mapping
is called concept drift. The data scientist
would need mechanisms (automated, as
above) to detect and correct for concept
drift. While this field is evolving (even
a precise definition of concept drift
eludes the machine learning community
today), the data scientist must closely
follow developments – like methods that
trigger detection of drift, synthetic drift
data generators for model testing, and
new classifications of drift handling and
correction methods.
Conclusions
In this article we increase the awareness
of the knowledge worker for the
motivations for the evolution of data
sciences. We have discussed the
statistical properties of homogeneity and
stationarity. We advise the data scientist
to prepare to apply techniques from
of data sciences for infinite data sets
such as ensemble models, AutoML and
concept drift
INSIGHTZ - VOLUME 03, 2018