In the realm of AI/ML, managing data is just as crucial as building a model. However, inconsistencies can arise when tracking changes or identifying which dataset was used for training runs or reports.
Understanding Version-Controlled MLOps
MLOps combines machine learning with version control systems to manage the entire ML lifecycle, from data preparation to deployment and monitoring.
This approach ensures that every change made to a model is tracked and recorded, allowing for reproducibility and collaboration among teams.
