What is training-serving skew

Machine learning has rapidly grown in popularity over the past few years, with increasing numbers of businesses and organizations looking to gain valuable insights from their data. However, one of the challenges that emerges as data sets get increasingly large and complex is the training-serving skew problem.

Training-serving skew refers to the differences between the data that is used to train machine learning models and the data that the models are ultimately applied to. In other words, the data used during training may have different statistical properties than the data that will be encountered in the real-world application of the model. This can lead to a number of problems, including biased predictions, poor generalization, and decreased accuracy.

One of the reasons that training-serving skew can occur is due to changes over time. For example, if a machine learning model is trained on data from a specific time period, but is then applied to more recent data, it is possible that the statistical patterns and relationships in the data may have shifted. Additionally, data acquisition and cleaning practices can also contribute to differences in training and serving data.

To address the problem of training-serving skew, there are a number of recommended best practices that machine learning practitioners can follow. One key step is to carefully consider the data distribution during the training phase, so as to ensure that the model can generalize well to new and unseen cases. This may involve using a more diverse set of training data, or prioritizing metrics that are more robust to changes in the data distribution.

Another useful strategy is to use a validation set, which can help to identify potential issues with overfitting or biased predictions during the training phase. Validation sets allow the machine learning team to test the performance of their models on data that is held separate from the training data.

Finally, it’s important to regularly monitor the performance of machine learning models in production, and to adapt the models as needed to address any issues that arise. This may involve updating the training data periodically, or fine-tuning the models based on new insights that emerge from the serving data.

In conclusion, training-serving skew is a critical problem facing machine learning practitioners, as it can lead to biased predictions, poor generalization, and decreased accuracy. However, with careful attention to best practices and a focus on ongoing monitoring and adaptation, it is possible to mitigate these issues and ensure that machine learning models are effectively serving the needs of businesses and organizations.