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AI meets operations

One of the biggest challenges operations groups will face over the coming year will be learning how to support AI- and ML-based applications. On one hand, ops groups are in a good position to do this; they’re already heavily invested in testing, monitoring, version control, reproducibility, and automation. On the other hand, they will have to learn a lot about how AI applications work and what’s needed to support them. There’s a lot more to AI Operations than Kubernetes and Docker. The operations community has the right language, and that’s a great start; I do not mean that in a dismissive sense. But on inspection, AI stretches the meanings of those terms in important but unfamiliar directions.

Three things need to be understood about AI.

First, the behavior of an AI application depends on a model, which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. Source code is relatively less important compared to typical applications; the training data is what determines how the model behaves, and the training process is all about tweaking parameters in the application so that it delivers correct results most of the time.

Second, the behavior of AI systems changes over time. Unlike a web application, they aren’t strictly dependent on the source. Models almost certainly react to incoming data; that’s their point. They may be retrained automatically. They almost certainly grow stale over time: users change the way they behave (often, the model is responsible for that change) and grow outdated.

Last, and maybe most important: AI applications are, above all, probabilistic. Given the same inputs, they don’t necessarily return the same results each time. This has important implications for testing. We can do unit testing, integration testing, and acceptance testing—but we have to acknowledge that AI is not a world in which testing whether 2 == 1+1 counts for much. And conversely, if you need software with that kind of accuracy (for example, a billing application), you shouldn’t be using AI. In the last two decades, a tremendous amount of work has been done on testing and building test suites. Now, it looks like that’s just a start. How do we test software whose behavior is fundamentally probabilistic? We will need to learn.

To support and collaborate AI application and operation, Artificial Intelligence World Society Innovation Network (AIWS-IN) created AIWS Young Leaders program including Young Leaders and Experts from Australia, Austria, Belgium, Britain, Canada, Denmark, Estonia, France, Finland, Germany, Greece, India, Italy, Japan, Latvia, Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Spain, Sweden, Switzerland, United States, and Vietnam.

The original article can be found here.