Session Outline

1. Prediction of impending failures using historical sensor data by applying advanced techniques like machine learning.
2. Machine learning aids in diagnosing the failure and helps the operations as well as the maintenance teams on the field.
3. The journey from Machine learning proof of concept to successful machine learning deployment and its challenges.

Key Takeaways

  • Application of machine learning in live time-series big data.
  • What methods worked and what doesn’t work.
  • Understanding the operational constraints and working around them
  • Keeping customers and stakeholders involved is the important key to any solution.



Rajat Kumar – Principle Data Scientist | ST ENGINEERING | Singapore

Data science and analytics leader, with 4 years of leadership experience and over 8 years of experience in Data Science, Machine Learning, data analytics, and data engineering. Enabling the connection of business and data world. Leading & managing Data Analytics projects/programs achieving long-term goals. Worked on Machine learning generic algorithm for quality improvement and virtual quality testing, general anomaly detection and solving complex non-linear machine learning problems on highly biased/imbalanced data. Have been a keynote speaker at many international conferences and have conducted data analytics online sessions.

January 12 @ 15:45
15:45 — 16:15 (30′)

Stage 2

Rajat Kumar – Principle Data Scientist | ST ENGINEERING | Singapore