Have you ever coded an ML pipeline which was taking a lot of time to run?

Or worse: have you ever got to the point where you needed to save on disk intermediate parts of the pipeline to be able to focus on one step at a time by using checkpoints?

Or even worse: have you ever tried to refactor such poorly-written machine learning code to put it to production, and it took you months?

Well, we’ve all been there working on machine learning pipelines for long enough.

So how should we build a good pipeline that will give us flexibility and the ability to easily refactor the code to put it in production later?

Read the full post here:
https://www.neuraxio.com/en/blog/neuraxle/2019/10/26/neat-machine-learning-pipelines.html