Kickstarting your ModelOps journey

Your ability to understand how your business is evolving is directly proportional to how your AI model is performing. With this mind, it’s vital to ensure that this ‘boring’ AI is kept fit and effective.

The feature engineering function is starting to take a front seat in the ModelOps as teams begin to implement and roll out feature stores which enable the critical operations of feature generation, feature governance (lineage and monitoring), as well as feature injection as part of model serving.

Investment in the feature store is critical if your business is to reap the benefits of AI. It’s the absolute cornerstone of your approach to ModelOps and will be the deciding factor between whether your AI is able to scale from a handful of models, to many thousands of them, all executing decisions in real time.

Bank vaults allow us to store money in a safe, secure environment, allowing us to revert back to it to count it at a later date, or take it out when we want to. The same can be said of the AI decision vault — an immutable store of all your business’ AI-based decisions.

It’s vital to ensure that your AI is kept fit and effective. The first step in doing so is understanding the need to consistently curate your AI models. To do this, you need an effective ModelOps team.



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