In the business context of machine learning, Data Contracts are vital protectors of model performance. They establish concise criteria for data quality, volume, timing, and structure. Their purpose is to guarantee that machine learning models always obtain the appropriate data for superior performance, despite changing data environments.
Machine learning models require consistent, high-quality data for training and inference. Variations in data quality or structure can lead to model performance degradation.
Data contracts define the quality, volume, timeliness, and data structure required for machine learning pipelines. They ensure that the data feeding into models is suitable for the task and that changes in the data are appropriately managed to avoid impacting model accuracy.