Synthetic Data and the Data-centric Machine Learning Life Cycle

Gretel's synthetic data platform helps overcome challenges across the data-centric machine learning life cycle, enabling successful AI projects. The process includes stages such as data collection, exploratory data analysis, data preparation and annotation, model training and evaluation, model deployment, model monitoring, and re-training. Gretel Synthetics can generate anonymized training datasets, measure dataset quality, augment stale training data, create synthetic demo datasets, test model responses to new inputs, and generate training data for data drift and concept drift in production.