3 research outputs found
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
Machine learning (ML) - based software systems are rapidly gaining adoption
across various domains, making it increasingly essential to ensure they perform
as intended. This report presents best practices for the Test and Evaluation
(T&E) of ML-enabled software systems across its lifecycle. We categorize the
lifecycle of ML-enabled software systems into three stages: component,
integration and deployment, and post-deployment. At the component level, the
primary objective is to test and evaluate the ML model as a standalone
component. Next, in the integration and deployment stage, the goal is to
evaluate an integrated ML-enabled system consisting of both ML and non-ML
components. Finally, once the ML-enabled software system is deployed and
operationalized, the T&E objective is to ensure the system performs as
intended. Maintenance activities for ML-enabled software systems span the
lifecycle and involve maintaining various assets of ML-enabled software
systems.
Given its unique characteristics, the T&E of ML-enabled software systems is
challenging. While significant research has been reported on T&E at the
component level, limited work is reported on T&E in the remaining two stages.
Furthermore, in many cases, there is a lack of systematic T&E strategies
throughout the ML-enabled system's lifecycle. This leads practitioners to
resort to ad-hoc T&E practices, which can undermine user confidence in the
reliability of ML-enabled software systems. New systematic testing approaches,
adequacy measurements, and metrics are required to address the T&E challenges
across all stages of the ML-enabled system lifecycle
A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning
Machine learning (ML) is increasingly being deployed in critical systems. The
data dependence of ML makes securing data used to train and test ML-enabled
systems of utmost importance. While the field of cybersecurity has
well-established practices for securing information, ML-enabled systems create
new attack vectors. Furthermore, data science and cybersecurity domains adhere
to their own set of skills and terminologies. This survey aims to present
background information for experts in both domains in topics such as
cryptography, access control, zero trust architectures, homomorphic encryption,
differential privacy for machine learning, and federated learning to establish
shared foundations and promote advancements in data security