Artificial intelligence projects in high-compliance industries, like healthcare and life science, often require processing Protected Health Information (PHI). This may happen because the nature of the projects does not allow full de-identification in advance – for example, when dealing with rare diseases, genetic sequencing data, identify theft, or training de-identification models – or when training is anonymized data but inference must happen on data with PHI.

In such scenarios, the alternative is to create an “AI cleanroom” – an isolated, hardened, air-gap environment where the work happens. Such a software platform should enable data scientists to log into the cleanroom, and do all the development work inside it – from initial data exploration & experimentation to model deployment & operations – while no data, computation, or generated assets ever leave the cleanroom.

This webinar presents the architecture of such a Cleanroom AI Platform, which has been actively used by Fortune 500 companies for the past three years. Second, it will survey the hundreds of DevOps & SecOps features requires to realize such a platform – from multi-factor authentication and point-to-point encryption to vulnerability scanning and network isolation. Third, it will explain how a Kubernetes-based architecture enables “Cleanroom AI” without giving up on the main benefits of cloud computing: elasticity, scalability, turnkey deployment, and a fully managed environment.

Presented by Ali Naqvi - Lead product manager of the AI Platform at John Snow Labs

Ali Naqvi is the 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.