The Boring Work of Implementing ML Models

The Boring Work of Implementing ML Models

We forget the day to day work needed to make that future into reality. What should you do with the data? They are countless articles talking about how ML will change the world. But we forget the work needed to make that into reality.

As I write blog posts of the potential for AI to help industries. Or some hype article in the media talking about how AI can help revolutionise the world or your favourite industry. We forget the day to day work needed to make that future into reality.

What should you do with the data?

Yes, having AI look at medical images is great and gives more accurate predictions than doctors. But how do you get that data? Medical data is hard to obtain for good reason. Patients medical history should not be passed around willy-nilly. As its very sensitive information. This is not just data about your music preferences. It’s about their lives. After you get the data. How should you train the data? A simple 2D medical image may require a Convolutional neural network. The default for computer vision. How do you train the data? You will need labels. So, the computer knows what it’s looking at. A person with expertise in the field (a doctor). Will need to help label the images. Depending on the goal of the model, the doctor will need to point out items in an image. (for object detection). Or just give the general category of the image. (image classifier). Now you have trained the model. And if a model is good. If the model is correct more often than doctors. Then you can think about how to move the model to production.

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