A paradigm shift is occurring in the world of CI/CD (Continuous Integration/Continuous Development). It's called, "Continuous Training," and it's a key aspect to machine learning development pipelines.
I'm currently studying data science at MIT and finding there's no tool more valuable to a data scientist than machine learning (other than inferential statistics and paper, but not going there!). Machine learning is a powerful tool that can help organizations solve complex problems and gain insights from data. However, machine learning models are not static. They need to be updated and retrained as new data become available, or as its environment changes. And this is where Machine Learning Operations (MLOps) comes in.
MLOps is a set of practices that aims to automate and streamline the entire machine learning lifecycle, from development to deployment and monitoring. MLOps builds upon the principles of DevOps, which focuses on continuous integration and continuous delivery (CI/CD) of software applications. CI/CD pipelines enable developers to test, integrate and deploy code changes quickly and reliably.
MLOps adds a new phase to CI/CD pipelines; it's called continuous training (CT). Continuous training is the process of automatically retraining and updating machine learning models based on new data or feedback. Continuous training ensures that the models are always accurate, relevant and aligned with the business goals.
Some examples of continuous training are:
A recommender system that updates its model based on the latest user behavior and preferences.
A fraud detection system that adapts its model to new patterns of fraudulent activity.
A sentiment analysis system that learns from new social media posts and reviews.
Here are ways continuous training fits into the MLOps pipeline:
Data ingestion: The pipeline collects and preprocesses new data from various sources.
Model training: The pipeline triggers a model retraining job based on predefined criteria, such as data volume, data quality or performance metrics.
Model validation: The pipeline evaluates the retrained model against a validation dataset and compares it with the previous model.
Model deployment: The pipeline deploys the retrained model to production if it meets the acceptance criteria, such as accuracy, latency or robustness.
Model monitoring: The pipeline monitors the performance and behavior of the retrained model in production and provides feedback for further improvement.
By implementing continuous training in MLOps pipelines, organizations can benefit from faster time to market or mission, improved customer/constituent satisfaction, reduced operational costs and enhanced innovation.