MLOps involves handling three key artifacts: data for preparing high-quality datasets, models for retraining and optimizing, and code for automated and repeatable processes.
Automating the preparation of high-quality datasets and retraining models to improve performance are crucial aspects of MLOps.
Tools are essential for searching for ML model parameters, saving model information, organizing model delivery, and monitoring model quality in MLOps.
Efficiently managing code is a crucial aspect of MLOps, enabling the automation and repeatability required for successful machine learning operations.
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