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From Jupyter to Production: Deploying an Influenza Monitoring System at Scale With Wearable Sensors
Speaker: Filip Jankovic

Summary
This talk walks through developing and deploying a machine learning pipeline at scale to predict flu onset in a production setting. Leveraging the open-source tools nbdev and Ploomber, we developed a workflow that allows us to produce maintainable, robust, production-ready machine learning pipelines directly from Jupyter.

Description
Direct to individual infection monitoring programs are transforming how infections are identified, measured, and treated. Models built on permissioned wearable sensor data from devices such as Fitbit, Garmin, and Apple Watch, can be used to notify individuals of potential infections early on.

Overview:
- Background and overview of the domain.
- Data and analytics architecture overview.
- Discuss the previous workflow and the challenges of taking a research model and creating a production pipeline. a. The complexity of translating between notebooks and production codebase b. Manual and inefficient tracking of pipeline status, outputs, and metadata c. Difficult to maintain repetitive, non-modular code
- How open-source tools (Jupyter, nbdev, and Ploomber) helped us solve previous challenges. a. Notebook-based development promotes rapid development. b. Ploomber orchestrates workflows and facilitates the handoff between notebooks and production-ready code.

Filip Jankovic's Bio
Filip is a Senior Data Scientist on Evidation's Research, Analytics, and Learning – Product team. Filip works on designing and developing data science products to bring novel health insights to individuals.

Filip's research areas include developing digital representations of health and wellness, the impact of interventions on health and engagement, and quality assessments of novel data streams.

Recent projects have focused on developing novel deep learning time series methods and productionizing machine learning models to deliver personalized insights to millions of individuals.

LinkedIn: https://www.linkedin.com/in/fjankovic/

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