Join us to learn why Human-in-the-Loop training data should be powering your machine learning (ML) projects and how to make it happen. If you’re curious about what human-in-the-loop machine learning actually looks like, join Figure Eight CTO Robert Munro and AWS machine learning experts to learn how to effectively incorporate active learning and human-in-the-loop practices in your ML projects to achieve better results. You'll learn: - When to use human-in-the-loop as an effective strategy for machine learning projects - How to set up an effective interface to get the most out of human intelligence - How to ensure high-quality, accurate data sets

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Practical Human-in-the-Loop
machine learning
Tuesday, May 22, 2018 | 10:00 AM PT
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Chris Burns, AI/ML Specialist Solutions Architect, Amazon Web
Services, Inc. (AWS)
Robert Munro, Chief Technology Officer, Figure Eight, Inc.
Today’s speakers
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• An overview of machine learning (ML) solutions offered through
AWS and the AWS Partner Network
• Featured AWS Machine Learning Competency Partner: Figure
Eight Technologies Inc.
• Q&A / Discussion
Today’s agenda
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Learning objectives
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• When to use Human-in-the-Loop as an effective strategy for machine
learning projects.
• How to set up an effective interface to get the most out of human
intelligence.
• How to ensure high-quality, accurate data sets.

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Machine learning on AWS
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Fulfillment & logistics
Search &
discovery
Existing products
New products
At Amazon,
we’ve been making
investments in
machine learning for
the last 20 years…

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Our mission:
Put machine learning in the
hands of every developer and
data scientist

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Source: McKinsey Global Institute, Artificial Intelligence The Next
Digital Frontier.
• Strong overall appetite for
adopting AI
• Top heavy in High Tech due to
expertise
• Opportunities exist in Health
Care, Education, Retail, and other
segments
• 3000+ startups today (up from 100
in 2011)
Market adoption: $46B market by 2020

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A long heritage of machine learning at Amazon
Personalized
recommendation
s
Inventing
entirely new
customer
experiences
Fulfillment
automation
and inventory
management
Drones Voice driven
interactions

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New: Amazon Rekognition Video
Object and activity
detection
Person
tracking
Face
recognition
Real-time live
stream
Content
moderation
Celebrity
recognition
Video analysis

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Amazon Machine Learning Stack
Vision
Frameworks &
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU
IoT (Amazon
Greengrass)
Platform
Services
Application
Services
Amazon
SageMaker
AWS
DeepLens
Amazon
Rekognition
Image
Amazon
Rekognition
Video
Speech
Amazo
n
Polly
Amazon
Transcribe
Language
Amazon
Translate
Amazon
Comprehend
Amazo
n
Lex
Amazon Machine
Learning
Amazon Spark on
Amazon EMR
Amazon
Mechanical Turk
TensorFlow GluonApache MXNet Cognitive Toolkit Caffe2 & Caffe PyTorch Keras

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Customers running machine learning on AWS
today

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The AWS Competency Program is designed to
highlight APN Partners who have demonstrated
technical proficiency and proven customer success
in specialized solution areas. Attaining an AWS
Competency allows partners to differentiate
themselves to customers by showcasing expertise in
a specific solution area.
W H AT IS TH E AW S
C OMPETEN C Y PR OGR A M?
The AWS Machine Learning Competency

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Figure Eight:
Generate high-quality training data for
continuously improving machine
learning at scale
Robert Munro, Chief Technology Officer, Figure Eight

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Figure Eight is a powerful Human-in-the-Loop AI
platform
for data science and machine learning teams.

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Human-in-the-Loop
• What it is: Humans and machines working together to
develop training data for machine learning.
• Humans label initial dataset, humans tune the model.
• Continuous feedback loop: Humans validate output and
correct inaccurate predictions from the model.

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Why Human-in-the-Loop is preferable
• Needed to make AI work in the real world
• 97%+ accuracy and quality is a necessity
• Safety, legal and financial implications

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Training data for machine learning
• Computer vision
• Natural language processing
• Data enrichment
• Data categorization
• Audio & speech recognition

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Image annotation for computer vision

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Image annotation for computer vision

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Image annotation for computer vision

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Image annotation for computer vision

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Human-in-the-Loop solutions
• Finance
• Manufacturing
• Medical
• Retail & CPG
• Security
• Aerial Imagery
• Autonomous
Vehicles
• Agricultural Tech
• Chatbots

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E-commerce Financial
services

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Media IT Robotics and
automotive

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E-Commerce
Business metric: Improved search results
Business metric: Increased site conversion

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Narrowing the business problem: break it down
Improve site conversion
Improve search results
Improve click through on top 10 search results returned
Improve % of hero images which feature the product

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Product Images
Products are shown by themselves with a white
background.
Lifestyle Images
Products are shown in the context of a room with
other furniture.

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Narrowing the business problem: break it down
Improve site conversion
Improve search results
Improve matching on text search strings to product titles
Improve categorization of products by filtering categories
Improve % of products which match categories searched

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Enterprise Software
Business metric: Improved customer experience
Business metric: Process automation

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Narrowing the business problem: break it down
Improve customer experience
Improve expense submission process
Reduce manual time to enter receipt information
Automate recognition of price, date, merchant and address
Improve text recognition by improving BB localization

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Robotics + IOT
Business metric: Business process automation

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Narrowing the business problem: break it down
Improve customer experience & repeat customer
business
Improve store experience
Improve % of shelves fully stocked at 100%
Understand which products are under-stocked
Delineate between stocked and empty shelves and
identify products missing

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Q & A

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Learn more about Figure Eight on AWS
• www.figure-eight.com
Learn more about machine learning on AWS
• aws.amazon.com/machine-learning/featured-partner-solutions
Try AWS for free:
• aws.amazon.com/free
Next steps and further information:
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Thank you!
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