Designing Data Pipelines for Healthcare AI: From Ingestion to Inference medium.com
Creating the data pipeline in healthcare AI is essential since it will influence the success rate of any model used. The article describes the reasons why data in the healthcare industry requires special handling during the process of building data pipelines because of the sensitivity, fragmentation, and lack of structure in health data. Each stage of the data pipeline is analyzed, including data ingestion, preprocessing, storage, inference, deployment, and MLOps. As a result, it becomes evident that successful healthcare AI depends not only on models but also on data architecture.
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