Why Do Machine Learning Projects Fail?
Think ahead to production so that you don’t let your machine learning project collapse before it even gets started
As data scientists, one of our jobs is to create the whole design for any given machine learning project. Whether we’re working on classification, regression, or deep learning project, it falls to us to decide on the data preprocessing steps, feature engineering, feature selection, evaluation metric, and the algorithm as well as hyperparameter tuning for the said algorithm. And we spend a lot of time worrying about these issues.
All of that is well and good. But there are a lot of other important things to consider when building a great machine learning system. For example, do we ever think about how we will deploy our models once we have them?
I have seen a lot of machine learning projects, and many of them are doomed to fail before they even begin as they don’t have a set plan for production from the onset. In my view, the process requirements for a successful ML project begins with thinking about how and when the model will go to production.
1. Establish a Baseline at The outset
I hate how machine learning projects start in most companies. Tell me if you’ve ever…
