Why are machine learning models degrade in production?

Why are machine learning models degrade in production?

To maintain high-quality models, algorithms should ideally be retrained with each data delivery. On the other hand, to optimize costs, it should be done as rarely as possible. Obviously, certain machine learning development practices incur more technical debt, hence entail more future maintenance than others.

What can be done about model degradation in data science?

The reasons for model degradation can be discovered and modeled explicitly. Recurrent temporal effects can be studied, understood, and exploited. This can be a project for the data science team to tackle once a model has gathered sufficient performance metrics. Well, assuming you’ve been tracking them.

How can I update my machine learning model?

Perhaps you can update the model each month or each year with the data collected from the prior period. This may also involve back-testing the model in order to select a suitable amount of historical data to include when re-fitting the static model. Another solution could be to weight data.

Are there any new approaches to image SR?

Recently, some approaches adopt generative adversarial networks (GANs) to relieve the above problems, but the resultant hallucinations and artifacts caused by GANs further pose grand challenges to image SR tasks. To address the above problems, reference-based image super-resolution (RefSR) is proposed as a new direction in the image SR field.

How are classification probabilities used in machine learning?

Many machine learning models can output classification probabilities. These indicate how “certain” a model is that this is the correct prediction. If these probabilities are relatively low, then the model might be struggling in deployment.

When to intervene in a machine learning model?

Model performance on fresh data sets should be evaluated regularly. These performance traces should be visualized and compared regularly so that you can identify when it’s time to intervene. Several metrics for evaluating ML performance exist. The reasons for model degradation can be discovered and modeled explicitly.

How does data drift affect a machine learning model?

This is especially true with regard to how a model performs over time with new training data. A model that was initially working pretty well could later degrade due to a concept called data drift or concept drift. Data drift occurs when the underlying statistical structure of your data changes over time.