ConocoPhillips is exploring the combination of machine vision and machine learning. Four proof of concepts were developed using AWS DeepLens, Amazon SageMaker, Amazon S3, and more. These projects address the security, safety, and inventory associated with upstream field operations. In this session, we describe our successes, challenges, and lessons learned. We also share our ideas for future product improvements.

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AIM205 - New AI/ML Solutions with
AWS DeepLens & Amazon SageMaker
with ConocoPhillips
Karla Wasinger
Cloud Cost Analyst
ConocoPhillips
A I M 2 0 5
Mahendra
Bairagi
AI Specialist SA
AWS
Blake Kobel
Analytics Analyst
ConocoPhillips
Corey Vessar
Analytics Analyst
ConocoPhillips

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Agenda
Introduction
Overview of AWS DeepLens and deploying a project
ConocoPhillips projects
Lessons learned

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Artificial Intelligence at Amazon

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PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Chainer Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker
Amazon
Rekognition
Amazon
Transcribe
Amazon
Translate
Amazon Polly
Amazon
Comprehend
Amazon Lex
AWS
DeepLens
EDUCATION
The Amazon Machine Learning Stack

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AWS DeepLens
is not a
video camera …
It’s the
world’s first
deep learning-enabled
developer kit

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AWS DeepLens Specifications
• Intel Atom Processor
• Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
• 8-GB RAM
• 16-GB storage (eMMC)
• 32-GB SD card
• 4 MP camera with MJPEG
• H.264 encoding at 1080p resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass preconfigured
• Intel clDNN Optimized for MXNet

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Get Started with Sample Projects
ARTISTIC STYLE TRANSFER
OBJECT DETECTION FACE DETECTIONHOT DOG / NOT HOT DOG
CAT VS. DOG
ACTIVITY DETECTION
Or build custom deep learning models in the cloud using
Amazon SageMaker
HEAD POSE DETECTION

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Under the Covers – Console

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Rig/Drill Site

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The Projects
Inventory Recognition
Bear Recognition Vehicle Probability
Hard Hat Detection

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Image Classification Vs. Object Detection

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What We Did
Tested with out of box models
Explored AWS services
Workshop with AWS Team
Image Classification -> Object
Detection
Trained custom models
Image annotation
Manual annotation
Scripts
AWS DeepLens Images
Fine tune
Project extensions

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Results
17
Bear RecognitionInventory Recognition

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Notifications
November 26, 201818
• Time
• Location
• Link to reorder
products
• Specific messages
• Multiple
individuals

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Inventory Demo

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Bear Demo

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Moving Forward
21
Machine Learning
• Collect Data Sets of Inventory
• Annotations
• Explore Amazon Rekognition
Logistics and Hardware
• Weatherization
• Hardware Upgrades
• Camera Type Options
• Additional Sensors

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23. Thank you!
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ConocoPhillips team:
Karla Wasinger, Blake Kobel, Corey
Vessar
DeeplensPOC@conocophillips.com
Mahendra Bairagi
bairagi@amazon.com

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