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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:
Here are ways continuous training fits into the MLOps pipeline:
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.