One of the most important things you need to consider is that your machine learning model is never finished. Model monitoring is a major issue because things like data drift can happen. In fact, the most successful companies have a model pipeline that considers this. You can easily know what is happening with model monitoring, and it isn’t that expensive for you to implement. You also know that maintaining model accuracy will become a major issue as time goes on, so you need to continually do things to keep it as good as possible.
An inaccurate model can cost your business millions or even billions of dollars. You need to assume that the predictive power of your model will diminish over time. Because of that, your monitoring solution needs to have a detailed look at how accurately your model is working, which means you can study things like data drift. It might seem simple, but doing this will severely impact your model accuracy over time.
Put It Into Your Planning
The first thing you can do to ensure that your model stays accurate over time is to support it in the planning stages. Your machine learning projects need to have a plan for data drift and for model accuracy to fall drastically. If your model pipeline includes this reality, you are more likely to fix things quickly. It will also help you prepare for these problems when they come.
The main issue is that most companies think that putting a model into production is the end goal. They never think about the model monitoring and other maintenance processes you need to do to ensure that model accuracy is always maintained at an acceptable level. Once you start understanding the benefit of planning, you can move on to the other stages of the model pipeline.
Retrain the Model
The second thing you can do to ensure your model is always accurate is to retrain the model periodically. Model accuracy doesn’t come overnight, and you cannot guarantee that your model pipeline will make your model accurate forever. Retraining the model is easy, and it is something you can do with new data in the case of data drift.
After you have retrained your model, you can monitor it to ensure that its predictive power takes a dramatic turn. These are the simple things you can do when retraining models. Model accuracy goes up, and you don’t need to do a lot of extra work to ensure that your project isn’t wasted.
Fix Your Entire Model Building Process
The final thing you might want to do is focus on the entire model pipeline. Data drift can cause problems with model accuracy, but you don’t want to have to retrain your model constantly. It might be easier for you to streamline the pipeline to be better. For example, you can improve the data to ensure that you always have accurate data at your fingertips.
You can even repair the model without having to do everything over again. That would mean finding the problem that causes the model to be degraded and then fixing it to ensure you get something that works for your needs. Ultimately, the most important thing is to make sure your machine learning model fits your business needs.