Description: The Model Lifecycle in the context of MLOps refers to the various stages a machine learning model goes through from its initial conception to its deployment and maintenance in a production environment. This cycle includes critical phases such as data collection, data preparation, model training, validation, deployment, and monitoring. Each of these stages is essential to ensure that the model is not only accurate but also robust and capable of adapting to changes in data or the operational environment. Effective management of the model lifecycle allows organizations to optimize their model development processes, ensuring quality standards are met and minimizing risks associated with the use of artificial intelligence. Furthermore, a model’s lifecycle is not linear; it may require continuous iterations and adjustments based on the model’s performance and feedback obtained during its use. In summary, the Model Lifecycle is an essential framework that guides data teams and machine learning engineers in creating and maintaining effective and efficient models over time.
