[AWS Techshift] Innovation and AI/ML Sagemaker Build-in 머신러닝 모델 활용 및 Marketplace 활용법 - 김무현, AWS 시니어 솔루션즈 아키텍트

© 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 Confidential and Trademark
Muhyun Kim, Data Scientist
Amazon ML Solutions Lab
What Amazon SageMaker
means to ISVs

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
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
& 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

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

ISV without AI/ML talents
• How can I differentiate our products by adding AI/ML features?
• I want to build AI products, but no ML expert is available.
AI ISV with AI/ML talents
• I want to monetize our ML algorithms or models?
Questions ISVs have

1. How to differentiate our
products by adding AI/ML
features?

FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
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
AWS Services to build AI powered applications

Identifying
family look-a-likes
FamilySearch uses Amazon Rekognition to analyze
family photographs and show users which ancestors
they most resemble.

Helping users
find parking
Mapillary uses Amazon Rekognition to analyze the 360+
million images in their database and extract text from
parking signs. That information enabled them to create
a new parking solution.

Saving buyers
money and helping
retailers succeed
Ibotta leverages Amazon SageMaker to train and
deploy machine learning models that power Search,
Personalization, and other foundational
infrastructure of their application.

1. BlazingText Algorithm
2. DeepAR Forecasting Algorithm
3. Factorization Machines Algorithm
4. Image Classification Algorithm
5. IP Insights Algorithm
6. K-Means Algorithm
7. K-Nearest Neighbors (k-NN) Algorithm
8. Latent Dirichlet Allocation (LDA) Algorithm
9. Linear Learner Algorithm
10. Neural Topic Model (NTM) Algorithm
11. Object2Vec Algorithm
12. Object Detection Algorithm
13. Principal Component Analysis (PCA) Algorithm
14. Random Cut Forest (RCF) Algorithm
15. Semantic Segmentation Algorithm
16. Sequence-to-Sequence Algorithm
17. XGBoost Algorithm
Built-in Algorithms in Amazon SageMaker
ML ModelDataset

Types of Machine Learning
Machine
Learning
• Training data is unlabeled
• Each example in training data has only features.
• Goal : Find hidden patterns in data.
Unsupervised
Learning
Supervised
Learning
• Training data is labeled
• Each example in training data has features and target.
• Goal : Predict target for new data.
Semi-Supervised
Learning
• Labeling training data is expensive
• Use un-labeled examples with small amount of labeled data.
Reinforcement
Learning
• No training data.
• State, action, reward and penalty.
• Goal : Learn right actions based on reward and penalty.

Types of Machine Learning
Machine
Learning
Unsupervised
Learning
Supervised
Learning
BlazingText Algorithm
K-Means Algorithm
K-Nearest Neighbors (k-NN) Algorithm
Latent Dirichlet Allocation (LDA) Algorithm
Principal Component Analysis (PCA) Algorithm
Random Cut Forest (RCF) Algorithm
DeepAR Forecasting Algorithm
Factorization Machines Algorithm
Image Classification Algorithm
Linear Learner Algorithm
Neural Topic Model (NTM) Algorithm
Object2Vec Algorithm
Object Detection Algorithm
Random Cut Forest (RCF) Algorithm
Semantic Segmentation Algorithm
Sequence-to-Sequence Algorithm
XG Boost Algorithm

K-Means Algorithm : When to Use
• Search Engines
• Customer Segmentation
• Segment by purchase history
• Segment by activities on application, website or platform
• Define profiles based on interests and activity
• Inventory Categorization
• Group inventory by sales activity
• Group inventory by manufacturing metrics

• Document classification and organization using similarity and relevance
• Document summarization
• Discovering underlying semantic topics from large data collections, be it of
texts, images, or even music notes
Latent Dirichlet Allocation (LDA) – When to use

Blazing Text – When to Use
• Used in Natural Language Processing (NLP)
• Sentiment Analysis
• Understand customers better.
• Identify product trends
• Machine Translation
• Provide multiple language support for websites
• Named Entity Recognition
• Identify organizations, main actors

Linear Learner – When to Use
• Discrete Categories (Classification)
• Based on past customer responses, should I mail this particular
customer? Yes/No
• Based on past customer segmentation, which segment does this
customer fall into? "empty nester," "suburban family," or "urban
professional."
• Quantitative Results (Regression)
• Based on the return on investment (ROI) from past mailings, what is the
ROI for mailing this customer

XGBoost – When to use
• eXtreme Gradient Boosting
• Based on gradient boosted trees algorithm
• Predict a target variable by combining the estimates of a set of simpler,
weaker models.
• Classification
• Regression
• Ranking

Factorization Machines – When to use

Image Classification
• Classifies an image into one of multiple output categories
• ResNet
• Very deep networks (152 layers by default)
• Two modes of operation
• Full Training
• Transfer Learning

DeepAR
• Time Series Forecasting
• Used internally at Amazon
• Minimal feature engineering
• Forecasts
• Point (amount sold was X)
• Probabilistic ( amount sold was between X and Y with Z probability)
• Demand for products
• Supply chain optimization
• Server load
• Web page requests

1. Formulate the business problem to solve
2. Identify and prepare dataset
3. Use SageMaker Ground Truth to label dataset for supervised ML model
4. Train your ML model using a built-in ML algorithm with dataset
What is needed to use built-in ML algorithm

How to use built-in algorithms
Training
Data
Built-in
algorithm
Amazon SageMaker
Ground Truth
Model
Real-time API
Batch
transformation
Amazon
SageMaker
Train
Labelling

Sample architecture
Amazon API Gateway AWS Lambda ML API Endpoint
Amazon SageMaker
User Mobile
client

2. How to monetize
our ML algorithms or models?

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
Object Detection Regression Text Clustering
Handwriting
Recognition
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

ML Algorithm
• An Amazon SageMaker algorithm
• Buyers use this to train their own models with their data
• Charge buyers for training and inference separately
ML Model Package
• An Amazon SageMaker model package
• Pre-trained model which buyers can use it immediately
• No training needed by buyers
• Charge buyers for inference jobs
What can be sold in AWS Marketplace for ML

How Algorithm and Model Package are used in
SageMaker

3 steps to sell ML algorithms or models

• Docker container with training and optionally inference code
• Configuration of input data for training
• Hyperparameters supported
• Metrics sent to Amazon CloudWatch during training
• Instance types, support of distributed training
• Validation profiles
• To ensure buyers and sellers can be confident that products work in
Amazon SageMaker
• To help buyers understand and evaluate the product before they buy
it
SageMaker Algorithm is ...

• Docker container with inference code
• Location of model artifacts
• Instance types, support of distributed training
• Validation profiles
• To ensure buyers and sellers can be confident that products work in
Amazon SageMaker
• To help buyers understand and evaluate the product before they buy
it
SageMaker Model Package is ...

aws-mp-bd-ml@amazon.com

ML on AWS
https://ml.aws
Get started with your AWS use case
https://aws.amazon.com/getting-started/use-cases/
AWS Marketplace - ML & AI
https://aws.amazon.com/marketplace/solutions/machinelearning/
AWS Solutions
https://aws.amazon.com/solutions/
Resources

Thank You!

[AWS Techshift] Innovation and AI/ML Sagemaker Build-in 머신러닝 모델 활용 및 Marketplace 활용법 - 김무현, AWS 시니어 솔루션즈 아키텍트

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