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Aligning DevSecOps and Machine Learning

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posted on 2024-03-12, 21:13 authored by Luiz AntunesLuiz Antunes

Over the past few years, society has seen a rebirth of interest in artificial intelligence (AI) and, more specifically, machine learning  (ML) applications. As is typical in time periods of hardware performance spikes in which computer scientists can increase the throughput of their systems, researchers have been using computers to learn from patterns in data that once took an impractical amount of time to process. This capability has not been limited to large, corporate entities; the advent of graphics processing units (GPUs) has enabled even non-corporate equipment to process large data sets. On a corporate scale, machines with multiple GPUs are used in data centers to mine information and identify data patterns like never before.  How can engineers organize such systems to take advantage of all the practices proposed by Agile methodologies and the more recent DevSecOps developments? In this blog post, I will explore the machine learning (ML) and DevSecOps domains and propose ways to use them in collaboration for increased performance.


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This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This report was prepared for the SEI Administrative Agent AFLCMC/AZS 5 Eglin Street Hanscom AFB, MA 01731-2100. NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution.



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