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Predictive Analytics with Amazon SageMaker

Steve Shirkey

Specialist SA, AWS (Singapore)


Explosion in AI and ML Use Cases

Image recognition and tagging for photo organization

Object detection, tracking and navigation for Autonomous Vehicles

Speech recognition & synthesis in Intelligent Voice Assistants

Algorithmic trading strategy performance improvement

Sentiment analysis for targeted advertisements

2


~1997


Thousands of Amazon Engineers Focused

on Machine Learning

Fulfillment &

logistics

Search &

discovery

Existing

products

New

products

At

AWS


• 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…


ML @ AWS

OUR MISSIONPut machine learning in the

hands of every developer

and data scientist


Customers Running Machine Learning On AWS Today


The Amazon Machine Learning Stack

PLATFORM SERVICES

APPLICATION SERVICES

FRAMEWORKS & INTERFACES

Caffe2 CNTKApache

MXNetPyTorch TensorFlow Chainer Keras Gluon

AWS Deep Learning AMIs

Amazon SageMaker AWS DeepLens

Rekognition Transcribe Translate Polly Comprehend Lex

Amazon EMR (Spark ML) Amazon Mechanical Turk


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

Data

Au

gm

en

tati

on

Featu

re

Au

gm

en

tati

on

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

Data

Au

gm

en

tati

on

Featu

re

Au

gm

en

tati

on

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

Data

Au

gm

en

tati

on

Featu

re

Au

gm

en

tati

on

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: 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: Model Deployment Undifferentiated Heavy Lifting


Amazon SageMaker


Amazon SageMaker

Easily build, train, and deploy machine learning models

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

Pre-built

notebooks

for common

problems

Set up and

manage

environments

for training

Train and

tune model

(trial and

error)

Built-in, high

performance

algorithms Deploy model

in production

Scale and

manage the

production

environment

BUILD



SageMaker Built-in

Algorithms

k-Means Clustering

PCA

Neural Topic Modelling

Factorisation Machines

Linear Learner

XGBoost

Latent Dirichlet Allocation

Image Classification

Seq2Seq

DeepAR Forecasting

BlazingText (word2vec)

Random Cut Forest

k-Nearest Neighbor

Object Detection

Training ML Models Using Amazon SageMaker


Bring Your Own

Algorithms

ML Algorithms

R

MXNet

TensorFlow

Caffe

PyTorch

Keras

CNTK

…

SageMaker Built-in

Algorithms

K-means Clustering

PCA



Linear Learner – Regression

XGBoost



Seq2Seq

Linear Learner –

Classification

DeepAR Forecasting



SageMaker

Framework SDKs

TensorFlow SDK

MXNet (Gluon) SDK

Chainer SDK

PyTorch SDK

SageMaker Built-in

Algorithms

K-means Clustering

PCA




XGBoost



Seq2Seq

Linear Learner –

Classification

DeepAR Forecasting

Bring Your

Own

AlgorithmsML Algorithms

R

MXNet

TensorFlow

Caffe

PyTorch

Keras

CNTK

…



SageMaker

Framework SDK

TensorFlow SDK

MXNet (Gluon) SDK

Chainer SDK

PyTorch SDK

SageMaker Built-in

Algorithms

K-means Clustering

PCA




XGBoost



Seq2Seq

Linear Learner –

Classification

DeepAR Forecasting

Bring Your

Own

AlgorithmsML Algorithms

R

MXNet

TensorFlow

Caffe

PyTorch

Keras

CNTK

…

Apache Spark

Estimator

Apache Spark Python library

Apache Spark Scala library

Amazon

EMR



Amazon SageMaker

Pre-built

notebooks

for common

problems

Built-in, high

performance

algorithms

One-click

training

Hyperparameter

tuning

BUILD TRAIN

Deploy model

in production

Scale and

manage the

production

environment



Amazon SageMaker

Fully managed

hosting with

auto-scaling

One-click

deploymentPre-built

notebooks

for common

problems

Built-in, high

performance

algorithms

One-click

training

Hyperparameter

tuning

BUILD TRAIN DEPLOY



SageMaker

Notebooks

Training

Algorithm

SageMaker

Training

SageMaker

Hosting AWS

Lambda

API

Gateway

Prepare

Training Data Inference requests

Amazon S3

Amazon S3

Train & Optimise Deploy

Raw

Data

Prepared

Data

Algorithm

Container

Trained

Model

Trained

Model

HPOUser

Interactions

Reference Architecture


Walkthrough: SageMaker Console


Amazon SageMaker Architecture


Amazon ECR

Model Training (on EC2)

Amazon SageMaker

Client application

Training code


Amazon ECR


Tra

inin

g d

ata

Training code Helper code

Client application

Training code

Amazon SageMaker


Amazon ECR


Tra

inin

g d

ata

Mo

de

l a

rtif

act

s


Client application

Inference code

Training code

Amazon SageMaker


Amazon ECR


Model Hosting (on EC2)

Tra

inin

g d

ata

Mo

de

l a

rtif

act

s


Helper codeInference code

Client application

Inference code

Training code

Amazon SageMaker


Amazon ECR



Tra

inin

g d

ata

Mo

de

l a

rtif

act

s



Client application

Inference code

Training code

Inference requestInference response

Inference Endpoint

Amazon SageMaker


Amazon ECR



Tra

inin

g d

ata

Mo

de

l a

rtif

act

s



Gro

un

d T

ruth

Client application

Inference code

Training code

Inference requestInference response

Inference Endpoint

Amazon SageMaker


Hyperparameter Tuning


Photo credit: https://www.flickr.com/photos/ceasedesist/5821282085 (Licensed for commercial use)


Learning Rate

Regula

rization

Option 1: Grid Search


Option 2: Random Search

Learning Rate

Regula

rizatio

n

Bergstra, Bengio, “Random Search for Hyper-Parameter Optimization”

https://dl.acm.org/citation.cfm?id=2188395

“Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time.”


Option 3: Bayesian Optimization Papers

• A Tutorial on Bayesian Optimization of Expensive Cost

Functions, with Application to Active User Modeling and

Hierarchical Reinforcement Learning

(https://arxiv.org/abs/1012.2599)

• Practical Bayesian Optimization of Machine Learning Algorithms

(https://arxiv.org/abs/1206.2944)

• Taking the Human Out of the Loop: A Review of Bayesian

Optimization (https://ieeexplore.ieee.org/document/7352306)


Hyperparameter Tuning in Amazon SageMaker

Amazon SageMaker’s Hyperparameter Tuning feature is

based on an implementation of Bayesian Optimization,

along with some additional optimizations


Walkthrough: Hyperparameter Tuning


Additional Resources


Next Steps with Amazon SageMaker

• Getting started with Amazon SageMaker:

• https://aws.amazon.com/sagemaker/

• Use the Amazon SageMaker SDK:

• For Python: https://github.com/aws/sagemaker-python-sdk

• For Spark: https://github.com/aws/sagemaker-spark

• SageMaker Code Samples / Workshops:

• https://github.com/awslabs/amazon-sagemaker-examples

• https://github.com/awslabs/amazon-sagemaker-workshop

https://aws.amazon.com/sagemaker/

https://github.com/aws/sagemaker-python-sdk

https://github.com/aws/sagemaker-spark

https://github.com/awslabs/amazon-sagemaker-examples

https://github.com/awslabs/amazon-sagemaker-workshop


Brainstorming Modeling Teaching

Leverage Amazon experts with decades of ML

experience with technologies like Amazon Echo,

Amazon Alexa, Prime Air and Amazon Go

Amazon ML Solutions

Lab provides ML

expertise

Amazon ML Solut ions Lab


AWS Training Offer

Make your data driven decisions count, and make a career in Big Data on AWS. Follow the Big Data Specialty learning path and become a specialist in Big Data:

• Implement core AWS Big Data services according to best practices

• Design and maintain Big Data

• Leverage tools to automate data analysis

Certified Cloud Practitioner

Associate-level Certification

AWS Certified Big Data - Specialty

• Enterprise solutions architects

• Data scientists

• Big Data solutions architects

• Data analysts

Who should attend

Free AWS digital training: Foundational knowledge

Big Data on AWS – 3-day Classroom Training

Free AWS digital training: Big Data Technology Fundamentals

Visit www.aws.training to find out more.

http://www.aws.training/


Q&A


We hope you found it interesting! A kind reminder to complete the survey.

Let us know what you thought of today’s event and how we can improve the

event experience for you in the future.

Thank You For Attending

AWS Data Driven Decisions Webinar Series.

[email protected]

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