Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up Loft

1© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved | 1© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Building Models for Satellite Imagery Using Amazon SageMaker and
xView/SpaceNet Dataset
Mike Liu
Solutions Architect – AI/ML
liumike@amazon.com
AWS | Federal Pop-Up Loft AI/ML 201

2© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
• 9-9:15 - Welcome & Introductions
• 9:15-10:15 - Introduction to Amazon ML Stack & SageMaker
• 10:15-10:30 - Break
• 10:30-11 - Lab 1: Create Your First SageMaker Notebook
• 11-12PM - Computer Vision 101
• 12PM-1 - Lunch
• 1-2 - Lab 2: xView Object Detection using Amazon SageMaker
• 2-3 - Lab 3: Training SpaceNet Building’s Dataset
• 3-3:15 - Workshop Cost Analysis
• 3:15-3:30 - Wrap up and Review
AGENDA

Welcome & Introductions

Introduction to Amazon ML Stack
& SageMaker

FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
THE AWS ML STACK
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
& C O M P R E H E N D
M E D I C A L
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
D L
C O N T A I N E R S
& A M I s
E L A S T I C
K U B E R N E T E S
S E R V I C E
E L A S T I C
C O N T A I N E R
S E R V I C E

AI Services
THE AWS ML STACK
ML Services
M E D I C A L
I M A G E
V I D E O
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 C E
D L
C O N T A I N E R S
& A M I s
E L A S T I C
K U B E R N E T E S
S E R V I C E
E L A S T I C
C O N T A I N E R
S E R V I C E

Bringing machine learning to all developers
AMAZON SAGEMAKER
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment

AMAZON SAGEMAKER
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems

TRAINING DATA
Successful
models require
high-quality data

TRAINING DATA

Build highly accurate training datasets and reduce data
labeling costs by up to 70% using machine learning
AMAZON SAGEMAKER GROUND TRUTH

AMAZON SAGEMAKER GROUND TRUTH

AMAZON SAGEMAKER
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
• K-Means Clustering
• Principal Component Analysis
• Neural Topic Modelling
• Factorization Machines
• Linear Learner (Regression)
• BlazingText
• Reinforcement learning
• XGBoost
• Topic Modeling (LDA)
• Image Classification
• Seq2Seq
• Linear Learner (Classification)
• DeepAR Forecasting

ML algorithms and models available instantly
AWS MARKETPLACE FOR MACHINE LEARNING
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

AMAZON SAGEMAKER
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training

AMAZON SAGEMAKER
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training Optimization

AMAZON SAGEMAKER
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
One-click
deployment

Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
One-click
deployment
Fully managed with
auto-scaling, health checks,
automatic handling
of node failures,
and security checks
AMAZON SAGEMAKER

One-click
model training
and deployment
Train once
run anywhere
10x
better algorithm
performance
2x
performance increases from
model optimization with Neo
70%
cost reduction for data
labeling using Ground Truth
75%
cost reduction for inference
with Elastic Inference
REDUCE COSTS INCREASE PERFORMANCE EASE-OF-USE
C U S T O M M A C H I N E L E A R N I N G F O R Y O U R B U S I N E S S
AMAZON SAGEMAKER

AI Services
THE AWS ML STACK
ML Services
M E D I C A L
I M A G E
V I D E O
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 C E
D L
C O N T A I N E R S
& A M I s
E L A S T I C
K U B E R N E T E S
S E R V I C E
E L A S T I C
C O N T A I N E R
S E R V I C E

HOW WE CAN HELP
• 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

QUESTIONS?

https://tinyurl.com/aiml201
Lab 1: Create Your First SageMaker Notebook

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark
CV-101 | Computer Vision Intro

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
The Machine Learning Journey
https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/

Supervised Learning
• The algorithm is given a set of training examples where the data and
target are known. It can then predict the target value for new
datasets, containing the same attributes
• Human intervention and validation required
Example: Photo classification and tagging
Buliding Car Person
OR OR

Supervised Learning : How Machines Learn
Input
Label
Machine
Learning
AlgorithmBuilding
Prediction
Car
Training Data
?
Label
Building
Adjust Model

How do we apply this to aerial
images?

Most Pre-Trained Models Don’t Work

Successful models require
high-quality training data

General Categories of Computer Vision Algorithms
1. Image Classification
• Convolutional Neural Network (CNN)
2. Object Detection
• Region-Based Convolution Neural Network (R-CNN)
3. Semantic Segmentation
• Fully Convolutional Network (FCN)
DogDog

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark
Convolutions!
(Who took Electrical Engineering courses in college?)

Let’s Walkthrough an Example….
Image Credit: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53

Region-Based Convolutional Neural Network
Image Credit: https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e

Fully Convolutional Network

What Are Some More Sophisticated Architectures?
• Image Classification
• ResNet
• Inception V4
• NASNet
• Object Detection
• Single Shot Multibox Detector (SSD)
• YOLOv3
• Feature Pyramid Networks (FPN) with Faster R-CNN
• Semantic Segmentation
• U-Net
• Mask R-CNN
• DeepLabv3+

Lab 2: xView Object Detection
using Amazon SageMaker

Lab 3: Training the SpaceNet
Building’s Dataset

ML Cost Example Analysis

WORKSHOP COST ANALYSIS (ASSUMPTIONS)
SageMaker Notebook Instance ml.m5.xlarge
Cost $ 0.269
Storage 5GB
Runtime 6hr
Data Transfer 5GB
SageMaker Model Training Instance ml.p3.8xlarge
Cost 17.136
Storage 50GB
Runtime 0.35hr
Data Transfer 5GB
SageMaker Endpoint Instance ml.m5.xlarge
Cost $ 0.269
Storage 5GB
Runtime 6hr
Data Transfer 5GB

WORKSHOP COST ANALYSIS
Service Description Region Cost/Event Event Qty Total
SageMaker Notebook Instance US East $ 0.269 Per Hour 6.00 $ 1.61
SageMaker Notebook Storage US East $ 0.140 GB-month 0.04 $ 0.01
SageMaker Notebook Data Transfer US East $ 0.016 GB 5.00 $ 0.08
SageMaker Model Training US East $ 17.136 Per Hour 0.35 $ 6.00
SageMaker Model Training Storage US East $ 0.140 GB-month 0.02 $ 0.00
SageMaker Endpoint US East $ 0.269 Per Hour 6.00 $ 1.61
SageMaker Endpoint Storage US East $ 0.140 GB-month 0.04 $ 0.01
SageMaker Endpoint Data Transfer US East $ 0.016 GB 5.00 $ 0.08
Total $ 9.40

WORKSHOP COST ANALYSIS (ASSUMPTIONS)
SageMaker Notebook Instance ml.m5.xlarge
Cost $ 0.269
Storage 5GB
Runtime 480hr
Data Transfer 5GB
SageMaker Model Training Instance ml.p3.8xlarge
Cost 17.136
Storage 50GB
Runtime 0.35hr
Data Transfer 5GB
SageMaker Endpoint Instance ml.m5.xlarge
Cost $ 0.269
Storage 5GB
Runtime 960hr
Data Transfer 5GB

WORKSHOP COST ANALYSIS (NO TERMINATION)
Service Description Region Cost/Event Event Qty Total
SageMaker Notebook Instance US East $ 0.269 Per Hour 480.00 $ 129.12
SageMaker Notebook Storage US East $ 0.140 GB-month 3.23 $ 0.45
SageMaker Notebook Data Transfer US East $ 0.016 GB 5.00 $ 0.08
SageMaker Model Training US East $ 17.136 Per Hour 0.35 $ 6.00
SageMaker Model Training Storage US East $ 0.140 GB-month 0.02 $ 0.00
SageMaker Endpoint US East $ 0.269 Per Hour 960.00 $ 258.24
SageMaker Endpoint Storage US East $ 0.140 GB-month 6.45 $ 0.90
SageMaker Endpoint Data Transfer US East $ 0.016 GB 5.00 $ 0.08
Total $ 394.87

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