GPSTEC201_Building an Artificial Intelligence Practice for Consulting Partners

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS re:INVENT
Bui l d i ng an Arti fi ci al Intel l i gence Practi ce
for Consul ti ng Partners
M a t t M c C l e a n | S o l u t i o n s A r c h i t e c t , A W S
R o b e r t M u n r o | V P M a c h i n e L e a r n i n g , C r o w d F l o w e r
G P S T E C 2 0 1
N o v e m b e r 2 7 , 2 0 1 7

What to expect from this session
 Promise of Artificial Intelligence
 Machine Learning process
 Consulting Partner opportunities
 Qualifying Machine Learning projects
 Building an AI team
 Data Annotation

“AI is the new electricity” — Andrew Ng

What can AI do?
A B
Supervised learning

What can AI do?
Email Spam (Yes/No)
Loan application Repay (Yes/No)
A B
Supervised learning

What can AI do?
Email Spam (Yes/No)
Loan application Repay (Yes/No)
Image Object labels
Text Audio
English French
A B
Supervised learning

The advent of
Deep Learning
Data

The advent of
Deep Learning
Algorithms
Data

The advent of
Deep Learning
Algorithms
GPU
& HPC
Data

A flywheel for data
Data

A flywheel for data
Data Analytics

A flywheel for data
Products
Data Analytics

A flywheel for data
Users Products
Data Analytics

A flywheel for data
More users Better products
More data Better analytics

Machine Learning &
Artificial Intelligence
Big Data
More users Better products
More data Better analytics
A flywheel for data

Value creation in Machine Learning
Supervised Learning
Transfer Learning
Unsupervised Learning
Reinforcement Learning
Structured Data
Unstructured Data
Value
DataAlgorithms

Machine Learning process

Circle of Machine Learning
Business Problem

Business Problem
Data Preparation

Data analytics are foundational to ML
Amazon S3 Amazon Redshift Amazon EMR AWS Glue

Business Problem
Data Preparation
Model
Development

Amazon AI for model development
Services
Platforms
Frameworks
Infrastructure
Apache
MXNet
Torch
Cognitive
Toolkit
KerasTheano
Caffe2
& Caffe
TensorFlow
AWS Deep Learning AMI
GPU MobileCPU IoT
Amazon ML ECS
Spark &
EMR
Kinesis Batch
Vision Speech Language

Business Problem
Data Preparation
Model
Development
Model Deployment

Model deployment options with AWS
Deployment
in the Cloud
Deployment
at the Edge

Characteristics of leading AI companies
Strategic data acquisition
Centralized data lake/warehouse
Focus on automation

Consulting Partner opportunities

Tactical engagement opportunities
Build data lake for customer to support enterprise data science
- AWS Quick Start guide
- AWS Training Course : Building a Serverless Data Lake
Bring about a quick win on a specific challenge; iterate
- Find low-hanging fruit
Facilitate customer enablement
- Support and run Machine Learning hackathons
Help automate the Machine Learning process
- Manage model training to deployment

Strategic Engagement opportunities
• Identify common business problems with standardized
statement of work
• Build a reference architecture for each vertical
• Develop artefacts that expedite time-to-outcome
• Provide model governance as a managed service

Advisory Consulting opportunities
C-level strategic consulting:
Data strategy: focus on data acquisition
Identify business processes that Machine Learning can improve
Launch new services leveraging Machine Learning

Qualifying Machine Learning projects

Qualifying questions
1. Is the problem domain complex enough to warrant machine
learning?

Good use cases for Machine Learning
Image recognition Spam filters
Fraud detection
Online advertising
Speech recognition
Product
recommendations

Amazon AI API Services
Amazon
Rekognition
Amazon
Lex
Life-like speech Image analysis Conversational
engine
Amazon
Polly

learning?
2. Does the customer have enough clean data?

Data attributes to consider
Data temperature is important
Start with clean and uniform data
For deep learning, a lot of data may be required

learning?
2. Does the customer have sufficient clean data?
3. Are there data labels available for Machine Learning to make
sense of the data?

learning?
2. Does the customer have sufficient clean data?
3. Are there data labels available for Machine Learning to make
sense of the data?
4. Is there an allowance of error in model predictions?

“All models are wrong, but some are useful”
—George Box

Building an AI team

Data Scientists are problem solvers
As a leader, you should:
• Build a diverse team
• Solve interesting problems
• Share your team’s science

What do Data Scientists do all day?
Most Machine Learning PhDs studied algorithms, but this is only 4% of
their time
CrowdFlower (2016):

What do Data Scientists do all day?
60% Cleaning and organizing data: “Data Janitors”?

How to keep your AI team happy?
Ensure everyone owns the most enjoyable tasks
Outsource the least enjoyable tasks

What you should expect from an AI team
50% data management and designing annotation
strategies for training data
20% working our how to deploy models at scale
20% adapting your models to your domain/problem
10% advanced math and theoretical Machine Learning

Data Annotation

Data Annotation
How important is training data?
How do we get training data?
How do we combine human and machine intelligence?

The importance of training data
More training data is often more important than the right algorithm
Banko and Brill
(2001)

The importance of training data
Deep Learning algorithms typically pass older methods only with a lot of
data
Ng (2016)

The hard thing about hard things
The problem with more data?
• The cost of compute halves every 18 months
• The cost of humans continually rises

Where does training data come from?
Source: XKCD

Ensuring quality data
1. Choice of workforce: crowd, trusted, NDA, and/or in-house?
Optimize for scalability or reliability

2. Embed test questions and ‘gold’ answers to track quality

3. Give multiple people one task and calculate agreement

How many people create training data?
100,000s of people
annotate data every
day, to train
machine learning
models
Photo: iMerit workers

What does training data look like?
Humans annotate
the data, so that
AI can see the
world the same
way

Active Learning: Human-in-the-loop
What went right and
wrong with the
deployed model?
What does this tell us
about additional data
to annotate for
training data?
Business Problem
Data Preparation
Model
Development
Model Deployment

Example use case: CrowdFlower and Giphy
Giphy: 200 million daily active
users, creating and sharing
animated gifs
Integration with Twitter,
Facebook, iPhone, Slack, etc.

Business problem:
Filter gifs by movie-like ratings:
“G”, “PG”, “PG-13”, “R”, “Explicit”
Hurdle:
Millions can be manually rated,
but not 10s of millions
Solution:
Human labeling plus Machine
Learning to scale labeling “Please rate this gif”

High value
images for
human
review
High volumes for
machine learning
predictions
Human labels
train ML model

High value
images for
human
review
High volumes for
machine learning
predictions
Human labels
train ML modelS3RDS
Deep Learning
AMI
AWS Lambda

Transfer Learning
Matthew Zeiler and Rob Fergus. ZF NET
RawPixels
ImageNetlabels
Using the output from one classifier in another

Transfer Learning
Matthew Zeiler and Rob Fergus. ZF NET
RawPixels
ImageNetlabels
SomeOtherLabels
(retrain just the last layer)
Adapting an existing Deep Learning model

Examples of other business problems
CrowdFlower is solving with this approach
Transport: self-driving cars
Agriculture: filtering good
from bad food via images
Disaster response:
identifying actionable
information
Search: image-matching
Chat-bots: speech
recognition and intent
classification
Retail: automatically
counting objects on shelves

Thank you!
A n y q u e s t i o n s , r e a c h o u t t o m m c c l e a n @ a m a z o n . c o m o r
r o b e r t . m u n r o @ c r o w d f l o w e r . c o m

GPSTEC201_Building an Artificial Intelligence Practice for Consulting Partners

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