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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