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ACM Opinion

How to Do Machine Learning without an Army of Data Scientists

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A colorful abstraction of digital transformation, AI, and other themes, with an eyeball at the center.

ML is still young enough that it lacks the mature tooling and workflow processes of traditional software development, where, concepts such as agile development, continuous integration, and continuous deployment let entrenched companies and scrappy startups quickly push new features to market.

The artificial intelligence (AI)/machine learning (ML) software development and deployment lifecycle is still very nascent. The challenge of moving models into production is exacerbated by a demand for speed and a shortage of qualified ML engineers. But there's hope that things may soon get better.

A new crop of platforms and tools are sprouting, defined loosely as MLOps, which is itself a derivative of DevOps.

From TechRepublic
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