Machine Learning (ML) is the subfield of this big, broad-sweeping category known as Artificial Intelligence. Machine learning gives machines the ability to improve their performance over time, without explicit intervention or help from a human being. With all these functionalities and Machine Learning magic, we need to be extra careful when applying machine learning in the industry. This is because machine learning problems are different from traditional problems, and machine learning is time-consuming too.
Let’s look at some reasons as to why one shouldn’t overuse Machine Learning.
Given a problem, try not to think if it can be solved using machine learning first. Instead, try thinking if I have enough data and if the problem can be solved using a few business rules.
If not, and there is scope for machine learning, try and check for AI services available with Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and so on. But, again, there are a lot of these readymade services available which take care of the infrastructure and scaling; all you need to do is API calls.
Sometimes you train a custom model with these service providers too. It’s easier to maintain, reliable, and scales, which is important for industry problems.
Even after this, if you feel your problem is specific and no service providers provide readymade solutions, try AutoML first. Then, we will go through a few AutoML examples. AutoML lets you create ML models easily and quickly and mostly do the tuning themselves.
Finally, after exhausting all these options, if you still feel you need your models, try and do it over a cloud VM or container. Again, those are easier to maintain, they are reliable, and they scale.
But some companies don’t trust cloud providers (their data needs to be secure and in-house) and do all of this in-house. In that case, be aware of the CI/CD requirement and production needs. Allocate time and resource for that also and not just model building.
Here’s what we recommend when you come across any business problem:
Hope this was helpful.