Business choices are increasingly being driven by machine learning algorithms. And, like any other business plan, these models must be altered over time, due to a technological phenomenon known as ‘ML Model Drift’. While most course curricula, papers, and postings specify a machine learning (ML) lifecycle that begins with data collection and ends with the deployment of the ML model in the appropriate environment, they overlook a critical aspect of the ML lifecycle: model drift.
The connection between the target variable and the independent variables evolves throughout time. The model becomes unstable as a result of this drift, and the predictions become increasingly inaccurate over time.
The easiest method to deal with this problem is to keep re-fitting the models. An estimate of when drift begins to seep into the model may be formed based on previous experiences. As a result of this, the model may be proactively re-developed to reduce the risks of drift.
Weighing data might be a viable alternative in circumstances when the data varies over time. Financial models that decide particular parameters based on recent transactions, for example, might include characteristics that give greater weight to current transactions while giving less weight to previous transactions. This not only guarantees that the model is stable, but it also helps to keep possible drift concerns at bay.
Modeling the change itself is a more complicated approach for combating model drift. The first model created is kept static and used as a starting point. New models can now be developed to correct the predictions of this baseline model as a result of a change in behavior in recent data.
Now that we’ve shown that the most typical option entails ongoing model retraining, the question of how often this should be done arises. There are several options for dealing with this, each of which varies based on the circumstances.
Occasionally, the issue will surface. While waiting for a problem to occur isn’t the most elegant solution, it’s the only choice when it comes to new models with no historical data to predict when things could go wrong. When a problem arises, an investigation into what went wrong may be conducted, and changes can be implemented to prevent such problems in the future.
Sometimes the data pertaining to the entities addressed in the model exhibit seasonal trends. In this case, the model needs are retrained based on the seasons. When it comes to holiday spending, credit lending institutions, for example, need to have particular models in place to deal with the abrupt shift inhabit.
Continuous model drift in ML monitoring, on the other hand, is the greatest technique to identify drift. Metrics relating to the model’s stability must be monitored on a regular basis. Depending on the domain and the business, this period might be a week, a month, a quarter, or even a year. The monitoring mode can be manual or automatic, with alerts and messages triggered anytime unexpected abnormalities are detected.
Drift in models may be divided into two kinds: data drift vs concept drift. The first is referred to as concept drift. When the statistical features of the target variable change, this occurs. As can be seen, if the definition of the variable we’re attempting to forecast changes, the model won’t operate as well for the new definition.
Data drift is the second and most prevalent type. This occurs when the predictors’ statistical features change. If the underlying variables change, the model will inevitably fail. When the patterns in the data alter owing to seasonality, this is a classic example of what may happen.
Whatever business concept is successful in the summer may not be successful in the winter. While demand for flights increases throughout the holiday seasons, airlines have a hard time maintaining occupancy during the off-seasons.