Predictive Maintenance: Feature Engineering for Manufacturing Data Scientists

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Topic: "Predictive Maintenance: The Power of Feature Engineering for Manufacturing Data Scientists"

Speaker: Hari Narayanan, Senior Data Scientist at dotData

As a customer-facing data scientist, he works in use cases involving the automotive, healthcare, retail, and insurance industries. Before joining dotData, he worked for top-tier OEMs including General Motors and Ford. He has published patents and publications and is also a recipient of the Technical Excellence Award at Ford. Together, he brings eight years of experience in the Automotive and Software industry. He has developed solutions for use cases involving Predictive Maintenance, Content Optimization, Inventory Optimization, Machine Life Optimization, and Customer Risk Prediction. Hari has received his Master’s degree in Industrial Engineering from Clemson University, specializing in Operations Research and Advanced Statistics.

Abstract:
Explore the untapped possibilities of Predictive Maintenance! Join data scientists, engineers, and manufacturing professionals on an engaging journey through predictive maintenance use cases, shedding light on this often underexplored realm of Machine Learning.

Hear from Hari Narayanan, a key member of dotData’s Data Science team, as he delves into the power of predictive analytics, machine learning, and feature engineering in the manufacturing landscape. Uncover the challenges of predictive maintenance use cases, challenging conventional approaches, and discover the potential of alternative methodologies like feature engineering.

Key Takeaways:
- Build accurate target variables
- Creating aggregated features
- Developing interaction-based features to model complex relationships

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