Retraining Machine Learning Models

Train, test, and deploy — that’s it, right? Is your work done? Not quite!

One of the biggest mistakes data scientists make with machine learning is that they assume their models will keep working properly forever after deployment. But what about the data, which will inevitably keep changing? A model deployed in production and left to itself won’t be able to adapt to changes in data by itself.

In a UK bank survey from August 2020, 35% of asked bankers reported a negative impact on ML model performance because of the pandemic. Unpredictable events like this are a great example of why continuous training and monitoring of ML models in production is important compared to static validation and testing techniques.

Deploying your models in production

A startup recently employed you as a lead data scientist to build a predictive model that determines housing prices in certain cities. Your model performance metrics look great, your app is ready to be deployed to production.

You successfully deploy your model. What’s next? Three things:

• Model serving
• Model performance monitoring
• Model re-training

What is continuous training?

Continuous training is an aspect of machine learning operations that automatically and continuously retrains machine learning models to adapt to changes in the data…