Training-serving skew: Difference between revisions

[[Training-serving skew]] is the difference between a [[model]]'s performance during [[training]] and that same model's performance during [[serving]] ([[inference]]). Training-serving skew is a common issue when [[deploying]] [[machine learning models]], particularly in production settings where they will be put to real world use. This term describes the difference in performance of a model during training and [[deployment]] that can arise from various sources such as different [[data distribution]]s, hardware configurations or software dependencies between these environments.

Another potential cause of training-serving discrepancy is differing hardware configurations between the training and deployment environments. For instance, if a model is trained on an efficient GPU but deployed onto a slower CPU, performance may suffer due to inefficiency when running on such less powerful hardware.

Finally, software dependencies between training and deployment environments can cause training-serving skew. For instance, a model may be trained using one version of a library, but deployed using another one; this could result in performance degradation as the model may not function as expected with the different library version.

There are several strategies to minimize training-serving skew in machine learning. One method is carefully monitoring model performance during deployment and making necessary changes based on this feedback. This may involve retraining the model with new data, reconfiguring hardware settings or updating software dependencies.

Okay! Imagine that you have a toy robot that you want to teach how to play with a ball. After teaching the robot the proper technique, test it to see if it learned it successfully; if so, congratulations - now both of you can enjoy some friendly competition!

But what happens when the toy robot goes to play with a friend's ball? If the ball is of different size or shape, it might not be able to match up well since it had only been trained on one type of ball before.

Machine learning works similarly to teaching and playing with a toy robot. To train a model, we show it examples of what we want it to do--just like teaching your toddler how to hit the ball. But when we use that model to make decisions or predictions - that's called serving--just like when the robot plays with its toy ball.

The issue arises if the data served to a model during serving differs from what it was trained on, then it may not perform optimally. This condition is known as "training-serving skew," and it's like the robot not being able to play with its new ball.