Wednesday, October 28, 2020
Applying AI to healthcare is a great opportunity — better predictions on who is more likely to develop diabetes, back pain, and other chronic diseases, better predictions on which patients will require hospital re-admissions — not only in saving money but also improving patient health. In this talk, we will discuss our technology solution and our challenges in building AI/ML solutions in this domain:
* We built a data ingestion and extraction process using Apache Beam and Google Cloud DataFlow. We will describe our obstacles around joining and normalizing disparate patient datasets and our heuristics to solve this problem. We will also talk about performance and scalability obstacles and our solutions.
* We built model training and serving pipelines using Kubeflow (TensorFlow on Kubernetes and Istio). We will talk about how we built a HIPAA/SOC2 compliant infrastructure with these technologies and our experience using Katib for model tuning.
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