Automating an end-to-end training solution
Like any other ML model, once an AutoML model is deployed and runs for a few months, it can benefit from being retrained. There are many reasons for this, in order of importance:
- ML models break if the pattern between your input data and target column changes. This often happens due to extraneous factors such as changes in consumer behavior. When the pattern breaks, you need to retrain your model to retain performance.
- ML models perform better the more relevant data you feed them. Therefore, as your data grows, you should periodically retrain models.
- Retraining models on a consistent basis means that they're less likely to break if patterns change slowly over time. Consequently, it's best practice to retrain as data is acquired.
In this section, you are going to put your skills to the test. You will be given a set of instructions similar to when you created an end-to-end scoring solution. However, this time...
