AI Model Governance in a High-Compliance Industry

Model governance defines a collection of best practices for data science – versioning, reproducibility, experiment tracking, automated CI/CD, and others. Within a high-compliance setting where the data used for training or inference contains private health information (PHI) or similarly sensitive data, additional requirements such as strong identity management, role-based access control, approval workflows, and full audit trail are added.

This webinar summarizes requirements and best practices for establishing a high-productivity data science team within a high-compliance environment. It then demonstrates how these requirements can be met using John Snow Labs’ Healthcare AI Platform.

About the speakers

Ali Naqvi

Lead Product Manager of the AI Platform at John Snow Labs

Ali has extensive experience building end-to-end data science platform & solution for the healthcare and life science industries, using modern technology stacks such as Kubernetes, TensorFlow, Spark, mlFlow, Elastic, Nifi, and related tools.

Ali has a Master’s degree in Molecular Science and over a decade of hands-on experience in software engineering and academic research.

Ali Naqvi

Lead Product Manager of the AI Platform at John Snow Labs

Ali has extensive experience building end-to-end data science platform & solution for the healthcare and life science industries, using modern technology stacks such as Kubernetes, TensorFlow, Spark, mlFlow, Elastic, Nifi, and related tools.

Ali has a Master’s degree in Molecular Science and over a decade of hands-on experience in software engineering and academic research.

August 19th at 2pm EST

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