Mastering Model Retraining in MLOps

Model retraining is a critical component of any robust MLOps stack, yet it is often overlooked. In this comprehensive guide, I’ll cover what model retraining is, why it’s needed, different retraining approaches, triggers, and best practices. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*TWh2d_9ONqUF-cwjbr7cwg.png" style="height:378px; width:700px" /> <h1>What is Model Retraining?</h1> Retraining refers to the process of creating a new model version by re-running the training pipeline on new data. This updates the model to reflect changes in the data over time. Without periodic retraining, model performance deteriorates due to: <ul> <li>Data drift — Statistical properties of incoming data change.</li> <li>Concept drift — The underlying relationships/mappings change.</li> </ul> Retraining mitigates these effects by incorporating new data. <a href="https://medium.com/cloudnloud/mastering-model-retraining-in-mlops-5cc8db324666">Click Here</a>