The Art and Science of Machine Learning Model Retraining


The Art and Science of Machine Learning Model Retraining Team
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One of the major benefits having effective MLOps solutions has brought to the table is the ability to do machine learning model retraining in production. Model retraining was always tricky because of how complicated the traditional machine learning pipeline used to be. With that complicated pipeline, it never made sense for companies to invest a lot of time into retraining a model. Despite how far we’ve come, it is still a little costly to retrain a model. That is why you need to figure out whether taking that path is the correct one when running machine learning in production.

With everything said, MLOps solutions have made it possible to retrain models quickly and on a shorter schedule. You can think of this benefit in the same way that DevOps has shortened the software engineering lifecycle. It means that models can be updated and retrained in short time spans, making products more accurate and longer-lasting. You have to do an analysis and figure out whether retraining will bring benefits that outweigh whatever costs you have

Why Should You Retrain Your Models?

The first and foremost reason to retrain your model is model draft. This can be characterized as a shifting world that causes your machine learning model to stop being accurate over time. When doing dynamic ML in production like that, you have to constantly fine-tune your model to keep up with these changes.

You might also need to use model retraining in cases where you don’t have enough data. In these cases, you might constantly be accumulating new data that you use to build a better model over time. However, you need to train that data and build another model to incorporate it into your running application.

Arguments Against Model Retraining

As with anything in life, there are major pros and cons to doing model retraining. The obvious benefit is that it produces better results when running ML in production, but you have to figure out whether the costs are worth those benefits.

Computational power is getting cheaper, but it still doesn’t cover the cost of humans putting in the work to produce the improved model. However, you can use an automated solution like to give you results without any additional human labor costs. Having exceptional MLOps solutions like this dramatically improves the benefits you get from doing things like model retraining. It is also important to remember that more complicated machine learning methods, like deep learning, will be more expensive.

Understanding Your Situation and Moving Ahead

Ultimately, you have to weigh the pros and the cons when figuring out whether to start model retraining. How much will that model retraining cost in salary, time, and computing power? What benefit will you gain from using it? These are the simple questions you have to ask when figuring out whether that machine learning model is ripe for being retrained. It might seem simple enough, but these questions can have a profound impact on your projects. However, the great thing about all of this is that MLOps solutions have brought down the cost of model retraining dramatically.

About the Author Team
Enterprise AI/ML Application Lifecycle Management Platform


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