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How to Train Algorithms at Scale – Mass Customisation Explained

Knowledge is power, particularly for algorithms. Understanding the basics of how algorithms need to be trained and managed will allow you to avoid scalability issues that can seriously affect your company. It will ultimately give you the edge over your competition who might not have read this article.


Here’s How You Train An Algorithm


Firstly, you’ll need to write code that defines how the algorithm will learn. You want the algorithm to understand how to map the input that you give it to the output you expect.


Secondly, once you’ve written the code for how the algorithm will learn, you’ll need to train it with labeled items, which means that you give the algorithm data with both the input and the output. An example would be to feed the image of a cat to an algorithm while also saying that you’re expecting it to say “cat” in return.


Eventually, the algorithm will understand why a cat is a cat and not a dog, because it will learn the patterns underneath.


Thirdly, once the algorithm is trained, it needs to be deployed. This means it should be made available to use for other people. Otherwise, you would be the only one to understand it! So you’ll need to make it ready for use and distribute it, for example by uploading it to the cloud.


Finally, the algorithm is ready and can be given data that is unlabelled (i.e. does not have the output added to it). It will receive the input, make a prediction based on what it has learned, and return the output.


But Here’s the Problem


Training, deploying, and maintaining a single algorithm is a relatively easy task for the right person. But a company is likely to have many more algorithms.


For example: a company has advanced servomotors that help manufacture its goods. A lot relies on these servomotors, so the company asked a data scientist to write an algorithm that monitors whether the torque profile of its servomotors is within the normal parameters or not.


But each servomotor has a different torque profile. This means the data scientist needs to either write different code for each servomotor or train each servomotor on different data, and save different versions of the algorithm. For each servomotor a different version.


This is already laborious, but there’s the added difficulty that algorithms need to be retrained when new data comes in. This means new data has to be connected to each separate and relevant version, which will then have to be retrained individually and uploaded to the cloud again.


If you have ten algorithms, that’s a slow and inefficient process. If you have a hundred, it’s a full-time job. And this isn’t just a problem specific to servomotors, it’s a problem for every company that has entities with separate datasets.


Come the Solution


Luckily, you can avoid this bottleneck altogether, because the Algorithm Factory has the ability to mass customise all your algorithms.


Mass customisation is a set of services that connects all your data to your algorithms and trains each model on the relevant data. It saves each trained model and tells the database where that model can be found. All this happens automatically on the back-end of your technology.


This means you’ll only need to write and deploy code once, and can train or retrain the algorithms of the servomotors at the press of a single button. The Algorithm Factory will let mass customisation automatically select the relevant algorithm, produce an output, and return the result.


There will be no restrictions because of manual inefficiencies in the way that an algorithm needs to be trained. Algorithms will be easier to train, run, and manage, and your company will be able to make effective use of the many benefits of algorithms.


Want to know more about the Algorithm Factory and how we can help you manage your algorithms? Go to and book a demo today.



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