True or False: MLOps includes testing not only code but also data and model schemas.

MLOps, which stands for Machine Learning Operations, is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. One of the critical components of MLOps is ensuring the integrity and performance of the machine learning pipeline, which includes not just the code but also the data and model schemas involved.

By including testing for data and model schemas, MLOps ensures that data inputs conform to the expected formats and structures, minimizing the risk of errors during model training and inference. This is essential because discrepancies between the expected and actual schema can lead to model failure or degraded performance when the model is put into production.

Additionally, testing model schemas is equally important, as it verifies that the models adhere to predefined expectations regarding their architecture and specifications, which helps maintain consistency and reliability over time as models are updated or replaced.

Therefore, the assertion that MLOps includes testing not only code but also data and model schemas is indeed true, reflecting the comprehensive approach needed to ensure robust machine learning operations.