Author: Dinesh Nirmal / Source: VentureBeat
A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. So why the long face? Because 11 months later, the model was still sitting on a shelf.
That gap between great AI prototypes and AI in operation is starting to be a common theme as AI and machine learning make contact with the real world. The reason is … Actually, there are a lot of reasons, and we can look at a bunch of them, but underneath all the other reasons is the fact that data doesn’t sit still and never will.
Data changes as the world changes. Building an AI or machine learning model means building a way of looking at the world. But as the world and the data change, the models need to adapt. The CTO I met was realizing that building a great model is only the first step.
A model on its own is too brittle for the real world. It needs to live as a larger system that’s actually fluid. So how do we make AI systems that are fluid? By building them with five attributes in mind:
1. Managed
For AI and machine learning to do real and lasting work, they need thoughtful, durable, and transparent infrastructure. That starts with identifying the data pipelines and correcting issues with bad or missing data. It also means integrated data governance and version control for models. The version of each model — and you might use thousands of them concurrently — indicates its inputs. You’ll want to know, and so will regulators.
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