arrow_back Machine Learning Model Management with MLflow
Building a data pipeline inside and outside a vehicle
Submitted by Chaitanya Hegde (@chaitanyahegde) on Tuesday, 9 July 2019
Session type: Short talk of 20 mins
Ather 450 is a smart electric vehicle with data intensive features on the vehicle as well as on the cloud/mobile app.
On the vehicle, the on-board software uses the vehicle data to make decisions regarding the vehicle behaviour and safety, while giving some user delight features like auto-indicator.
Via the cloud, user has a mobile app using which the vehicle can be monitored and their ride statistics can be accessed. Data on the cloud is also processed for alerts, doing a root-cause analyses in case of vehicle issues and building towards a preventive maintenance system.
This is an overview of the data pipeline from the vehicle to the cloud database which enables this to happen.
- Intro to the Ather 450
- Inside the 450
- Inside the 450 - Features
- Inside the 450 - Data Architecture
- Inside the 450 - Learnings
- Outside the 450 - Features
- Outside the 450 - Data Pipeline Architecture
- Outside the 450 - Challenges and Approach
- Better Customer Service through data - Case Study
- Final Thoughts
Chaitanya Hegde, Product Manager - Software & Intelligence at Ather Energy. I work on building and improving software and smart features for the Ather 450 and future Ather platforms. When not working, I like to quiz, consume a lot of pop-culture and try to play the carnatic flute.