AI operationalization is different than traditional deployment and putting things into “production”. But if it’s different for deployment and different in development, then it follows that AI projects are unlike traditional projects even with regards to QA. Simply put, you don’t QA AI projects like you QA other projects. This is because the concept of what we’re testing, how we test, and when we test is radically different in AI Projects.
When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don’t call it deployment. Instead the term that’s used in the industry is “operationalizing”. This might be confusing for traditional IT operations managers and applications developers. Why don’t we deploy or put into production AI models? What does …
This post was featured in our Cognilytica Newsletter, with additional details. Didn’t get the newsletter? Sign up here One of the core things we focus on in our Cognilytica AI & Machine Learning training and certification is that machine learning projects are not application development projects. Much of the value of machine learning projects rest …
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