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Dattaraj Rao

Bangalore, India

I am an Engineer. I love to Learn, explore new Technology and solve real-world problems. I have been with GE for 18 years part of Global Research, Power and Transportation.

Currently, I am leading the Analytics and Artificial Intelligence (AI) Strategy for Transportation Digital. This involves identifying opportunities to enhance new and existing Products and drive outcomes like Predictive Maintenance, Machine Vision and Digital Twins. We are building a state-of-the-art Machine Learning platform to address major Data Science concerns like Data Cleansing, Preparation, Model Selection, Hyper-parameter tuning, Distributed Training and Automated Deployment. Collaborating with strategic Partners to build components for this Kubernetes Platform aimed at Cloud and On-Premise deployment. Also driving Edge Analytics Strategy including selection of hardware acceleration chipset - evaluating solutions like NVIDIA GPU and Google TPU.

I led the Global team that incubated Video-based Inspection for the Railway Track from idea to a commercial offering. This involves building a Digital Twin of the Railway Network by analysing live video feeds using Deep Learning and Computer Vision. We incubated this idea out of Bangalore and have 10 patents on this Technology.

I also played the role of Innovation Leader for Bangalore Engineering team of 400+ Engineers. Helped teams incubate ideas like Virtual Operator Cab, Augmented Reality (AR) inspection and Engine/Turbocharger Prognostics - and translate into funded programs.

Dattaraj Rao Points
Academic 0
Author 6
Influencer 2
Speaker 0
Entrepreneur 0
Total 8

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Areas of Expertise

Analytics 30.28
Big Data 30.09
Cloud 30.14
Emerging Technology
Predictive Analytics
AI 30.05
AR/VR 30.44

Industry Experience


6 Article/Blogs
A Platform for Machine Learning
December 03, 2018
Our general assumption is that Data Science is all about Algorithm development and Model selection. Not quite true in today's BigData world. If you have a neatly packaged CSV file as data source then yes, you can spend quality time figuring out how different features correlate with each other and extract patterns using Machine Learning (ML). However, today’s data Scientist usually spends more than 50% time dealing with other Concerns (areas of focus) like Data Preparation, Cleansing, Distributed Training, Model Deployment and Scaling.

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Tags: AI, Big Data, Cloud

To GPU or TPU...that's the question!
October 26, 2018
DISCLAIMER: This post is not a reflection of views of General Electric. This is just a personal hobby post I have written to share knowledge on this topic.

It's pretty much universally accepted that to effectively Train large Deep Learning Models we need some sort of hardware acceleration over the CPU. Models with many Neural Layers of Learning like Convolutional Neural Nets (best for image analysis) and Recurrent Neural Nets (for sequence data like text or time-series) - involve huge number of parallel operations during Training and Inference. The hardware acceleration engines have 1000s of cores for handling basic Linear Algebra calculations in parallel. NVIDIA is the leader in this space with their tried and tested GPUs. GPUs come in many form factors to server processing needs in the Data Center and for Edge devices.

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Tags: Analytics, AR/VR, Big Data

Quick (and free) experiment for CPU vs GPU for Deep Learning
August 01, 2018
Recently I presented at some NVIDIA conferences and got many questions on GPU vs CPU - Is the GPU really worth it? Does it really provide the benefits advertised? To be honest, we were skeptical before getting a desktop GPU for training our Video Analytics models. However, once we started using it and saw ours models zip through the training process in seconds compared to minutes (and sometimes hours) - we were definitely convinced. You need to have a solid case where you will be continuously training models to justify a local version - but for general experimentation you can always start with a Cloud instance.

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Tags: Analytics, AI, Cloud

Statistics, Machine Learning and a scenic Train journey
June 14, 2018
I have been preparing content for an Analytics class I will be teaching shortly. During this curation process had some interesting discussions with folks on real-world use-cases and all the jargon like Null hypothesis, Hypothesis testing, p-Value, Z-score, Supervised, Unsupervised, Training & Test datasets, Correlations, etc. Figured I should consolidate my learnings into this post and get views from a wider audience. Mainly to get your feedback and opinions and see if I got anything wrong (very possible).

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Tags: Analytics, AR/VR

Listen to your machines using Predix
January 31, 2018
Check out my latest Blog post on Was a simple experiment we started with on collecting and analyzing machine sounds in Predix Cloud. Many more things can be done with this audio data - Lots of potential here!

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Tags: AI, Cloud

Deep Learning on Predix for Computer Vision
November 06, 2017
Hi folks - I just published a video tutorial showing a very simple way to build a web application on GE Predix Platform. This app reads images uploaded and extracts Knowledge from these using a pre-trained Deep Learning model. This application can detect major objects in the image and highlight them with a degree of confidence.

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Tags: Analytics, AI

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