Which cloud servicer provider ML platform do you need?

Which cloud servicer provider ML platform do you need?

AWS Sagemaker, Azure ML platform or GCP AI platform? It actually doesn’t matter. Not for industrialisation.

First, I’m going to assume that you have chosen a cloud service provider (CSP), or in the position to choose one for your organisation. Secondly, I’m also assuming that you need to be able to build, train, tune, evaluate and deploy a machine learning model, then the first thing you are most likely to do is check out the ML platform of your CSP of choice. Or should you look at all those third-party vendors? How to compare?

Let’s look at what actually matters, namely, the bigger picture.

Experimentation vs Industrialisation

In each ML or even data science project, there are two phases with a fundamentally different goal. During experimentation, the goal is to find a model that answers a (business) question as fast as possible. Whereas during industrialisation the goal is to run reliably and automatically.

Let’s assume an organisation prioritises operational excellence first over business value. Maybe after 2 years, they have a model in production that brings value, but runs like a charm. Most (innovation) projects are killed well before, because no demonstrated value has been shown.

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First prove experimentation, then industrialisation. (Image by author)

So a trajectory that makes sense is one that you see in green in the figure above. Go for the low-hanging fruit first using very basic services that are maybe not suited for production use, but at least value has been shown. It could be that the first 4 project in that space are clear fails, but the fifth one is a huge hit.

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