Cyber Defense eMagazine February Edition for 2023

Secondly, MLOps platforms record all modeling results and decisions. Model development is moved from
individual laptops and onto a platform that allows oversight of every model that is deployed. We often
skip this supervisory step when MLOps platforms are not available, and oversight is key to trust.
Speaking of deployment, if an MLOps platform includes a deployment API, then the tool is able to validate
that the data on which the model is evaluated in practice is from a similar statistical distribution to the
data on which the model was trained. By automating alerts on low-confidence results, we can ensure that
models are only applied where they can provide reliable results.
A fourth measure is in contributing to the successful implementation of data governance policies. The
DOD is currently actively developing data policies to increase transparency, reliability, and discoverability
of datasets, while decreasing model bias that may follow from data misuse.
Using today’s models tomorrow
Advances in ML are staggering and impressive, and we cannot expect that a growing workforce are all
equally able to understand advances in state-of-the-art solutions. Rather, we will rely on a few highly
educated engineers within an organization to follow research that is relevant to that group’s success, and
to implement related software solutions.
As an organization’s MLOps platform becomes updated to take advantage of emerging research, the
repeatable nature of the MLOps documented data to deployment processes will allow an easy adjustment
to existing deployed models so that every deployed model is derived from current state-of-the-art
modeling approaches. Similarly, as analysts accumulate new data, models can easily be retrained to
take advantage of our most current understanding. Platforms additionally should support a workflow that
automatically retrains deployed models based on emerging data, with guardrails against failing sensors
or maliciously manipulated data streams.
The defense community relies on immense amounts of data as a vital resource for their daily operations.
MLOps platforms create the opportunity to propagate employee knowledge with confidence and improve
the performance of these important models to enable better decision making across the defense space.
Carefully selecting an MLOps solution will allow organizations to move beyond the simple models that
automate daily tasks and evolve with critical data to navigate the challenges of future missions.
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