Introduction to Amazon SageMaker End-to-End ML Platform

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Introduction to
Amazon SageMaker
End-to-End Managed ML Platform
Osemeke Isibor,
Solutions Architect.
iosemeke@amazon.com
5th September 2018

Machine Learning Example
Dear Customer,
This special offer for this amazing car is for you!

Historical Data set
AGE GENDER LOCATION TEST DRIVE
24 M CENTRAL Y
42 F KOWLOON N
33 M LANTAU N

New Data Set
AGE GENDER LOCATION TEST DRIVE
24 M CENTRAL ?
42 F KOWLOON ?
33 M LANTAU ?

Query for Result
SELECT * FROM customers
WHERE
(age =24 AND gender =‘M’ AND location =‘Central’)
OR
(age =42 AND gender =‘F’ AND location =‘Kowloon’)
OR
(age =33 AND gender =‘M’ AND location =‘Lantau’)
OR
.. . .N

Supervised Machine Learning Process Flow
Age Gender Location TEST
DRIVE
30 M Central Y
40 M Chai Wan N
…. …… ….. …..
Learning
Algorithm
Model
Output
Historical Purchase Data
(Training Data)
Prediction
Age Gender Location TEST
DRIVE
35 F Lantau
39 M Taikoo
Input - New Unseen Data

Model Training - Performance Measurement
All Labeled Dataset
Training Data
70% 30%
Training
Test
Data
Evaluation
Result
Trial
Model
Accuracy

We Call This Approach Machine Learning

Machine Learning – When to Use It
You need ML if:
•Simple classification rules are inadequate
•Scalability is an issue with large number of datasets
You do not need ML if:
•You can predict the answers by using simple rules and computations
•You can program predetermined steps without needing any data driven
learning

Supervised Learning
It is a cat.
No, it’s a
Labrador.

Supervised Learning – How Machine Learn
Human intervention and validation required
e.g. Photo classification and tagging
Input
Label
Machine
Learning
Algorithm
Labrador
Prediction
Cat
Training Data
?
Label
Labrador
Adjust Model

Unsupervised Learning
No human intervention required
(e.g. Customer segmentation)
Input
Machine
Learning
Algorithm
Prediction

Machine Learning is a Powerful Tool
Learning Language Perception Problem Solving Reasoning

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

Customer Running ML on AWS Today

The broadest ML platform, that’s easiest to use, and
has the most customers
APPLICATION SERVICES
Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Rekognition Image
Amazon Rekognition Video
PLATFORM SERVICES
Amazon SageMaker
AWS DeepLens
FRAMEWORKS AND INTERFACES
AWS Deep Learning AMI
Apache MXNet
Caffe2
CNTK
PyTorch
TensorFlow
Theano
Torch
Gluon
Keras
Amazon ML Service
Amazon Mechanical Turk

APPLICATION SERVICES
Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Rekognition Image
Amazon Rekognition Video
PLATFORM SERVICES
Amazon SageMaker
AWS DeepLens
FRAMEWORKS AND INTERFACES
AWS Deep Learning AMI
Apache MXNet
Caffe2
CNTK
PyTorch
TensorFlow
Theano
Torch
Gluon
Keras
Amazon ML Service
Amazon Mechanical Turk
The broadest ML platform, that’s easiest to use, and
has the most customers

Let’s Review the ML Process

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

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

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 Architecture
Retraining
• Build the data platform:
• Amazon S3
• AWS Glue
• Amazon Athena
• Amazon EMR
• Amazon Redshift
Spectrum

Analysis
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
• Setup and manage
Notebook Environments
• Setup and manage
Training Clusters
• Write Data Connectors
• Scale ML algorithms to
large datasets
• Distribute ML training
algorithm to multiple
machines
• Secure Model artifacts
Why We built Amazon SageMaker: The Model Training Undifferentiated Heavy Lifting

Model Deployment
Monitoring &
Debugging
– Predictions
• Setup and manage Model
Inference Clusters
• Manage and Scale Model
Inference APIs
• Monitor and Debug Model
Predictions
• Models versioning and
performance tracking
• Automate New Model
version promotion to
production (A/B testing)
Why We built Amazon SageMaker: The Model Deployment Undifferentiated Heavy Lifting

A fully managed service that enables data scientists and developers to quickly and easily
build machine-learning based models into production smart applications.
Amazon SageMaker

End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale

Amazon SageMaker: Launch Customers

Amazon SageMaker: Launch Customers
“With Amazon SageMaker, we can accelerate our Artificial
Intelligence initiatives at scale by building and deploying our
algorithms on the platform. We will create novel large-scale
machine learning and AI algorithms and deploy them on this
platform to solve complex problems that can power prosperity for
our customers.
"
- Ashok Srivastava, Chief Data Officer, Intuit

Key benefits of SageMaker at Intuit
Ad-hoc setup and management
of notebook environments
Limited choices for model
deployment
Competing for compute
resources across teams
Easy data exploration
in SageMaker notebooks
Building around virtualization
for flexibility
Auto-scalable model hosting
environment
From To

Model Hosting
(SageMaker)
Near real -ti me fraud d etecti on i n AWS usi ng SageMaker
Calculate
Features
Reader
Cleanser
Processor
Data
Lookup
Training
Feature Store Model Training
(SageMaker)
Model
Client Service
Amazon
EMR
Amazon
SageMaker
Amazon
SageMaker

Amazon SageMaker
1 2 3 4
I I I I
Notebook Instances Algorithms ML Training Service ML Hosting Service

1
I
Notebook Instances
Zero Setup For Exploratory Data Analysis
Authoring &
Notebooks
ETL Access to AWS
Database services
Access to S3 Data
Lake
• Recommendations/Personalization
• Fraud Detection
• Forecasting
• Image Classification
• Churn Prediction
• Marketing Email/Campaign Targeting
• Log processing and anomaly detection
• Speech to Text
• More…
“Just add data”

Streaming datasets,
for cheaper training
Train faster, in a
single pass
Greater reliability on
extremely large
datasets
Choice of several ML
algorithms
Amazon SageMaker: 10x better algorithms
2
I
Algorithms

Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Single
Machine
Distributed, with
Strong Machines

Streaming
GPU State

Streaming
Data Size
Memory
Data Size
Time/Cost

Distributed
GPU State
GPU State
GPU State

Shared State
GPU
GPU
GPU Local
State
Shared
State
Local
State
Local
State

Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Best Alternative
Amazon SageMaker

Infinitely Scalable ML Algorithms

Algorithm Type Popular Use case
Linear Learner Supervised Learning Classification and regresion problems
Factorization Machines Supervised Learning
Classification and regresion problems, recommendation
engines
Sequence to Sequence (Seq2Seq) Supervised Learning Language translation
K-means Clustering Unspervised Learning Grouping similar objects
Principal Component Analysis (PCA) Unspervised Learning
Identify hidden patterns in complicated data sets. Facial
comparison
Neural Topic Modeling Unspervised Learning Topic Modeling
DeepAR Forecasting Supervised Learning Time Series forecasting
BlazingText ( Text Classiication +
Word2vec)
Supervised +
Unsupervised NLP, sentiment analysis
XGBoost (extreme gradient boosting) Supervised Learning
Decision tree for Prediction, classification, ranking,
regresson
Image Classification Supervised Learning Classifies an image into one of multiple categories
Object Dection Supervised Learning Dectect object.
Latent Dirichlet Allocation (LDA) Unspervised Learning Topic Modeling
Random Cut Forest Unspervised Learning Anomaly detection, Fraud dection
K-Nearest Neighbours Supervised Learning Classification, medical diagnosis

Managed Distributed Training with Flexibility
Training code
• Matrix Factorization
• Regression
• Principal Component Analysis
• K-Means Clustering
• Gradient Boosted Trees
• And More!
Amazon provided Algorithms
Bring Your Own Script (IM builds the Container)
Bring Your Own Algorithm (You build the Container)
3
I
ML Training Service
Fetch Training data
Save Model Artifacts
Fully
managed –
Secured–
Amazon ECR
Save Inference Image
IM Estimators in
Apache Spark
CPU GPU HPO

4
I
ML Hosting Service
Amazon ECR
30 50
10 10
ProductionVariant
Model Artifacts
Inference Image
Model versions
Versions of the same
inference code saved in
inference containers.
Prod is the primary
one, 50% of the traffic
must be served there!
One-Click!
EndpointConfiguration
Inference Endpoint
Amazon Provided Algorithms
Amazon SageMaker
Easy Model Deployment to Amazon SageMaker
InstanceType: c3.4xlarge
InitialInstanceCount: 3
ModelName: prod
VariantName: primary
InitialVariantWeight: 50

4
I
ML Hosting Service
 Auto-Scaling Inference
APIs
 A/B Testing (more to
come)
 Low Latency & High
Throughput
 Bring Your Own Model
 Python SDK
Amazon SageMaker
Easy Model Deployment to Amazon SageMaker

Summary

Amazon SageMaker
!GO BUILD!
End-to-End Managed ML Platform

Introduction to Amazon SageMaker End-to-End ML Platform

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