Building and deploying AI/ML models on AWS for health professionals

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Building and deploying AI/ML
models on AWS for Biosciences
professionals
HelloAI Summer School
Javier Ramirez
@supercoco9

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Optical Nerve Damage & Macular
Degeneration
• Radiology Image Recognition
• Readmission Risk
• Clinical Decision Support (Diagnosis &
Treatment)
• Fitness Incentives
• Hospital Stay Length & Re-admittance
Predictions
• Pharmaceutical Sales Force Optimization
• Personalized Medicine
• Structure-based Drug Design
• Knowledge Curation for Drug Discovery
• Cohort Selection
• Etiology of Disease
• Disease Onset Likelihood
• Clinical Trial Intelligence
• Outbreak Prediction
• Anomaly Detection
• Computational Drug Design
• Pictogram Classification
• Patient Condition Forecasting
D i a g n o s i s & O u t c o m e s
Use Cases: Health & Life Sciences
D r u g D i s c o v e r y & O p s

3© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved | 3© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Identifying critical
medical forms
Beth Israel Deaconess Medical Center uses Amazon
SageMaker to identify completed patient consent
forms before a surgery, lowering the number of
delayed or cancelled procedures.

Advancing pharma
research and discovery
Celgene uses Apache MXNet on Amazon SageMaker
for toxicology prediction to virtually analyze biological
impacts of potential drugs without putting patients at
risk. A model that once took 2 months to train can
now be trained in 4 hours.

Reaching
new patients
Inhealthcare uses Amazon Polly to provide telephone-
based digital healthcare services, allowing them to serve
patients who are not digitally connected.

Helping patients
manage disease
Propeller Health uses Amazon SageMaker to build and
deploy personalized disease models, giving patients new
insights on symptoms, triggers and medication use.

Diagnosing
heart disease
Samsung SDS has trained a deep learning model using
Apache MXNet to accurately diagnoses cardiac arrhythmia
from heartbeat data. This technology helps cardiologists
make early diagnoses of heart diseases so they can
provide timely preventive care.

Helping doctors focus
on their patients
Suki uses Amazon Comprehend Medical to extract
structured data from medical transcripts, helping
streamline clinical workflows.

Improving clinical
trials to cure cancer
Fred Hutchinson Cancer Research Center uses Amazon
Comprehend Medical to extract key patient data from
medical records. This helps them develop better clinical
trials, then match trials with the best patients.
https://www.mtsamples.com/site/pages/sample.asp?Type=96-Hematology%20-
%20Oncology&Sample=2769-Mantle%20Cell%20Lymphoma%20-%20Consult

10© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
“For cancer patients and the researchers dedicated to curing
them, time is the limiting resource. The process of developing
clinical trials and connecting them with the right patients
requires research teams to sift through and label mountains of
clinical record data. The Amazon Comprehend Medical service
reduces this time burden from hours to seconds. This is a vital
step toward getting researchers rapid access to the information
they need when they need it so they can advance lifesaving
therapies for patients.”
Matthew Trunnell,
Chief Information Officer,
Fred Hutchinson Cancer Research Center

Driving better
healthcare outcomes
Using Amazon SageMaker, GE Healthcare developed
an ML model that can learn from thousands of
medical scans to detect anomalies more accurately
and efficiently, allowing radiologists to prioritize
patients needing immediate attention.

@dmbanga @apwenchel
Solving Some Of The Hardest Problems In Computer Science
Learning Language Perception Problem
Solving
Reasoning

@dmbanga @apwenchel
~1997

@dmbanga @apwenchel

@dmbanga @apwenchel
• Applied Research
• Core Research
• Alexa
• Demand Forecasting
• Risk Analytics
• Search
• Recommendations
• AI Services
• Q&A Systems
• Supply Chain Optimization
• Advertising
• Machine Translation
• Video Content Analysis
• Robotics
• Lots of Computer Vision..
• NLP / NLU
Over 20 years of AI at Amazon…

@dmbanga @apwenchel
Detecting Politicians

@dmbanga @apwenchel
FDA Approved Medical Imaging

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Customer-focused
90%+ of our ML roadmap is
defined by customers
Pace of innovation
200+ new ML launches
and major feature updates last year
Breadth and depth
A wide range of AI and ML services
Multi-framework
Support for the most
popular frameworks
Security and analytics
Deep set of security with robust encryption and
analytics
Embedded R&D
Customer-centric approach
Our Approach to Machine Learning

M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
Vision | Documents | Speech | Language | Chatbots | Forecasting | Recommendations
M L S E R V I C E S Data labeling | Pre-built algorithms & notebooks | One-click training and deployment
Build, train, and deploy machine learning models fast
Easily add intelligence to applications without machine
learning skills
Flexibility & choice, highest-performing infrastructure
Support for ML frameworks | Compute options purpose-built for ML

FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A W S M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4 E C 2 C 5 I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E

@dmbanga @apwenchel
ML Organizational Processes
How does machine learning fit in your organization?

@dmbanga @apwenchel
Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
The Machine Learning Process
Re-training

@dmbanga @apwenchel
Analysis
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Discovery: The Analysts
Re-training
• Help formulate the right
questions
• Domain Knowledge

@dmbanga @apwenchel
Analysis
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Integration: The Data Architects
Re-training
• Build the data platform:
• Amazon S3
• AWS Glue
• Amazon Athena
• Amazon EMR
• Amazon Redshift
& Spectrum

@dmbanga @apwenchel
Analysis
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Re-training
Modeling: The Data Scientists
• Builds the ML Models:
• Deep Learning AMI
• SparkML on EMR
• AI Services
• Amazon SageMaker

@dmbanga @apwenchel
Analysis
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Re-training
Production: The SDE and the DevOps
• Build Smart Apps
• AWS Lambda
• Amazon S3
• Amazon API Gateway
• AWS IoT
• Amazon Kinesis
• Amazon ECS/ECR
• Mobile Hub
• AWS KMS
• Amazon EC2
• More…

Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
1
2
3

1
2
3
Amazon SageMaker:

Amazon SageMaker:

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Built-in algorithms
pink: supervised, blue: unsupervised
Linear learner: regression, classification Image classification: deep learning (ResNet)
Factorization machines: regression, classification,
recommendation
Object detection: deep learning
(VGG or ResNet)
K-Nearest neighbors: non-parametric regression and
classification
Neural topic model: topic modeling
XGBoost: regression, classification, ranking
https://github.com/dmlc/xgboost
Latent Dirichlet allocation: topic modeling (mostly)
K-Means: clustering Blazing text: GPU-based Word2Vec,
and text classification
Principal component analysis: dimensionality reduction Sequence to sequence: machine translation, speech-to-text
and more
Random cut forest: anomaly detection DeepAR: time-series forecasting (RNN)
Object2Vec general-purpose embeddings IP Insights: usage patterns for IP addresses

AWS Marketplace for Machine Learning
Over 200 algorithms and models that
can be deployed directly to Amazon SageMaker

AWS Marketplace for Machine Learning
ML algorithms and models available instantly
Subscribe in a
single click
Available in
Amazon SageMaker
KEY FEATURES
Automatic labeling via machine learning
IP protection
Automated billing and metering
Browse or search
AWS Marketplace
S E L L E R S
Broad selection of paid, free, and
open-source algorithms and models
Data protection
Discoverable on your AWS bill
B U Y E R S

Over 200 algorithms and models
Natural Language
Processing
Grammar & Parsing Text OCR Computer Vision
Named Entity
Recognition
Video Classification
Speech Recognition Text-to-Speech Speaker Identification Text Classification 3D Images Anomaly Detection
Text Generation Object Detection Regression Text Clustering
Handwriting
Recognition
Ranking
A V A I L A B L E A L G O R I T H M S & M O D E L S
S E L E C T E D V E N D O R S

Amazon SageMaker Neo
Train once, run anywhere with 2x the performance

Amazon SageMaker Neo: Train once, run anywhere
Neo

Amazon SageMaker Neo
Train once, run anywhere with 2x the performance
K E Y F E A T U R E S
Open-source device runtime and compiler,
1/10th the size of original frameworks

Successful models require high-quality data

Amazon SageMaker Ground Truth
Build highly accurate training datasets and reduce data labeling costs
by up to 70% using machine learning

Amazon SageMaker Ground Truth
How it works

Sowhat’snextfor
machinelearning?
Howdoyouteachmachinelearningmodelstomakedecisions
whenthereisnotrainingdata?

Introducing Reinforcement Learning
Representation
of the real world
Programmed
to represent real-
world conditions
Enables interaction
with user or a
computer program
Dynamic and updates
itself based on the
interactions and
programmed behavior

Amazon SageMaker RL
New machine learning capabilities in Amazon SageMaker to
build, train, and deploy with reinforcement learning

• Build machine learning models
in Amazon SageMaker
• Train, test, and iterate on the track using
the AWS DeepRacer 3D racing simulator
• Compete in the world’s first global autonomous
racing league, to race for prizes and a chance to
advance to win the coveted AWS DeepRacer Cup
A fully autonomous 1/18th-scale race car designed to help you
learn about reinforcement learning through autonomous driving
A W S D E E P R A C E R

@dmbanga @apwenchel
Security & Encryption Integrated Across The Platform
Fine grained
access controls
Broad KMS
integration
Server-side
encryption
with CMK
Audit key
usage by
user & role
Import
keys
Policy
validation
& simulation

@dmbanga @apwenchel
Key AWS Certifications and Assurance Programs

Setting your organization
up for success
Culture

1
Create
the loop
Connect technology initiatives
with business outcomes
2
Assess your structured and
unstructured data sources
Advance your
data strategy
?
3
Put machine learning in the
hands of your developers
Organize
for success

https://github.com/aws-samples/amazon-comprehend-medical-image-
deidentification
Demo

Recap: Amazon SageMaker
Collect and
prepare training
data
Train and
tune models
Set up and
manage
environments
for training
Deploy models
in production
Scale and manage
the production
environment
Amazon EC2 P3
Instances
Amazon SageMaker RL
Amazon SageMaker
Ground Truth
Amazon Elastic
Inference
AWS Marketplace
for
Machine Learning
Amazon SageMaker
Neo
Single API call
for deployment

FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A W S M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4 E C 2 C 5 I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E

H O W W E C A N H E L P
• Brainstorming
• Custom modeling
• Training
• Work side-by-side with Amazon experts
ML Solutions Lab
• Practical education on ML for new
and experienced practitioners
• Based on the same material used
to train Amazon developers
Machine Learning
Training and Certification

Resources
- SageMaker Product Page
- SageMaker Console
- Ground Truth Product Page
- Neo Product Page
- SageMaker 10-Minute Tutorial
- SageMaker Related Blogs

Thank You!
Javier Ramirez
@supercoco9

Building and deploying AI/ML models on AWS for health professionals

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