Perhaps you don’t need a complex Machine Learning solution.
Many problems are not ML problems.
They might be optimization issues or exploratory data analysis tasks or problems that can be solved with simple statistics.
OK, now you are certain that you need ML.
Then, you need data!Many say that the steps outlined above are the most time consuming (thus the most expensive) in building ML solutions.
(At least some basic ETL will be required to run this part in a production environment).
After data is prepared, the autoML can be applied.
In one mouse-click you can train 100s or even 1000s of models which are then trained in parallel in the cloud.
You end up with a set of models, or even an ensemble of models, trained to optimize some metric with respect to some validation scheme.
The best single (heavily tuned) model will be a few percent better than a single model with default hyper-parameters.
An ensemble of all your models will give you the next few percentage points of improvement.
Let’s say you will end up with up to 10-25% improvement, pretty good, isn’t it?!.In same cases it is a lot and can be converted into huge ROI – think of trading solutions or credits scoring tasks.
But!.You end up with a complex model which is:What do you mean by model maintenance?.Well, it is not enough to train accurate and fancy AI/ML model.
For using the ML model in production you should:Training ML models is exciting!.This is what Tigger likes most!.Building software around ML is not as exciting.
The boring software needed for ML model to operate in production consists of:When running ML locally, the last two steps are usually connected.
All the software used needs some maintenance as well.
You were successful with all above steps.
Congratulations!.You can now compute predictions for your new data records.
What do you do with them?Bio: Piotr Plonski is a software developer from Łapy, Poland who mainly works with python and js.
Piotr likes solid software that is working as expected under different circumstances and has clean design.
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