Training a machine learning model to automate expense reporting Amazon Web Services

Before you can build a successful machine learning (ML) algorithm that works in the real world, you must ensure that your data is appropriately annotated by human annotators. Creating accurate training data helps ensure better accuracy when you launch your ML models into production. Accurate training data also makes it more likely that you are creating a model that achieves your business goal in the real world. Without the ingestion of quality training data, ML models are useless. Poorly-labeled training data leads to poor-performing algorithms.