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S U MM I TB E R L I N

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMIT

The Machine Learning Process:From Business Model to ML in Production

Constantin GonzalezPrincipal Solutions ArchitectAmazon Web [email protected] | @zalez


What you’ll get out of this session

• An overview of the Machine Learning (ML) process• From business model

• to data collection and processing

• to ML training and deployment

• … and all the way back

• A better understanding of how to apply ML to your business

• Examples from AWS customers and Amazon.com

SUMMIT © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.SUMMITSUMMIT © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

Put machine learning in the

hands of every developer

Our mission at AWS


Customers running machine learning on AWS


The Amazon Machine Learning stack

A I S E R V I C E S

M L S E R V I C E S

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 m a z o n

S a g e M a k e rG r o u n d T r u t h A l g o r i t h m s

N o t e b o o k s

M a r k e t p l a c e

U n s u p e r v i s e d

L e a r n i n g

S u p e r v i s e d

L e a r n i n g

R e i n f o r c e m e n t

L e a r n i n g

O p t i m i z a t i o n

( N e o )

T r a i n i n g

H o s t i n g

D e p l o y m e n t

Frameworks Interfaces Infrast ructure

A m a z o n

R e k o g n i t i o n

I m a g e

A m a z o n

P o l l y

A m a z o n

T r a n s c r i b e

A m a z o n

T r a n s l a t e

A m a z o n

C o m p r e h e n d

A m a z o n

L e x

A m a z o n

R e k o g n i t i o n

V i d e o

Vis ion Speech Language Chatbots

A m a z o n

F o r e c a s t

Forecast ing

A m a z o n

T e x t r a c tA m a z o n

P e r s o n a l i z e

Recommendat ions

A m a z o n

E C 2 P 3

& P 3 D N

A m a z o n

E C 2 C 5

F P G A s A W S G r e e n g r a s s A m a z o n

E l a s t i c

I n f e r e n c e

A m a z o n

I n f e r e n t i a

NEW NEW NEW

NEW

NEW NEW

NEW

NEW

NEW NEW


But how do I start?Where should I apply Machine Learning?What approach should I be taking?

?



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

Da

ta A

ug

me

nta

tio

n

Fe

atu

re

Au

gm

en

tati

on

Re-training

The Machine Learning process



Analysis



Data Integration

Data Preparation &

Cleaning

Feature Engineering

Model Training &

Parameter Tuning

Model Evaluation

Are

Business

Goals

met?

Model Deployment

Monitoring &

Debugging

– Predictions

YesNo

Da

ta A

ug

me

nta

tio

n

Fe

atu

re

Au

gm

en

tati

on

Re-training




The Amazon Flywheel



There’s an app a service for that …


The Amazon Flywheel

Product

recommendations



There’s a service for that: Amazon Translate


There’s a service for that: Amazon Translate


The Amazon Flywheel

Automatic

translation


Amazon Forecast


The Amazon Flywheel

Supply chain

optimization


The Amazon Flywheel

ML opportunityML opportunity

ML opportunity

ML opportunity


Build your own Flywheel






Checklist

1. Create a flywheel model of your business


Checklist


2. Find opportunities to add value through ML• Save cost through better planning

• Save cost through automating human work

• Increase revenue through delivering a better product/service

• Increase revenue through better customer experience


Common ML use cases for some verticals

• Retail: Supply chain and demand forecasting

• Financial services: Credit default prediction for customer behavior

• Manufacturing: Real-time predictions for industrial IoT

• Advertising: Predict click-through rate for targeted ads

• Media and branding: Prediction of language content quality

• Automotive innovation: Self-driving vehicles and simulation

• Health and wellness: Track disease progression




Analysis



Data Integration

Data Preparation &

Cleaning

Feature Engineering

Model Training &

Parameter Tuning

Model Evaluation

Are

Business

Goals

met?

Model Deployment

Monitoring &

Debugging

– Predictions

YesNo

Da

ta A

ug

me

nta

tio

n

Fe

atu

re

Au

gm

en

tati

on

Re-training



Data

every 5 years

There is more data than people think

15years

live for

Data platforms need to

1,000xscale

>10xgrows


Web/App

Classic

• Traditional databases, data warehousing

• Historical data, logs, archives

• Big data

Industry

• Production facilities

• Control devices

• IoT-Sensors

• Websites, web apps, mobile apps

• Enterprise applications (BI, CRM, etc.)

• External data (partners, weather, traffic, etc.)


Streaming

Import/Batch

Web/App

Classic

Industry


Data Lake

Streaming

Import/Batch

Web/App

Classic

Industry


Data Lake

Streaming

Import/Batch

EncryptionData catalog

classification

Access

control

Web/App

Classic

Industry


Data Lake

Streaming

Import/Batch

Web/App

Classic

Industry

ETL

Pre-processing

Machine learning


Data Lake

Streaming

Import/Batch

Web/App

Classic

Industry

ETL

Pre-processing

Machine learning Monitoring

Control

Applications

API


Data Lake

Streaming

Import/Batch

Building a data lake


Data Lake

Streaming

Import/Batch

AWS Direct ConnectAWS SnowballAWS SnowmobileAWS Database Migration Service

On-premises Data Movement


Data Lake

Streaming

Import/Batch


AWS IoT CoreAmazon Kinesis Data FirehoseAmazon Kinesis Data StreamsAmazon Kinesis Video Streams


Real-time Data Movement


Data Lake

Streaming

Import/Batch





Amazon AthenaAmazon EMRAmazon RedshiftAmazon Elasticsearch ServiceAmazon KinesisAmazon QuickSight

Analytics


Data Lake

Streaming

Import/Batch





Amazon AthenaAmazon EMRAmazon RedshiftAmazon Elasticsearch ServiceAmazon KinesisAmazon QuickSight

Analytics

Amazon SageMakerAmazon RekognitionAmazon ComprehendAmazon TranslateAmazon TranscribeEtc.

Machine Learning


There’s a service for that: AWS Lake Formation

Data Lake Storage

Data

Catalog

Access

Control

Data

import

Lake Formation

Crawlers ML-based

data prep

Use Amazon S3 as the

storage layer for

Lake Formation

Ask Lake Formation

to create required

S3 buckets and

import data into them

Register existing

S3 buckets that

contain your data

Data is stored in

your account:

You have direct access.

No lock-in.

Amazon S3


Checklist


2. Find opportunities to add value through ML

3. Collect training data from your data lake• Production data

• Web/app data

• Legacy/archive data


Here.com uses Amazon Rekognition toenrich their mapping content

“We use Amazon Rekognition to enrich our mapping content.

Rekognition’s Text in Image allows us to continually update signage

information so our customers have the latest information at their

fingertips. We look forward to continuing our partnership with AWS and

implementing their computer vision solutions in more of our products.”

–Rajkumar Jain

Director of Engineering at HERE Technologies


Supervised Learning

Data


Supervised Learning

Data Labels


Supervised Learning

Data Labels


Supervised Learning

Data Labels

Images: Unsplash.com


Supervised Learning

Data Labels


Supervised Learning

Data Labels

Cheap!


Supervised Learning

Data Labels

Cheap! Expensive!


Supervised Learning

Data Labels

• Historical data

• Log files

• Transactions

• Data Warehouse


Supervised Learning

Data Labels

• Operational data

• Streaming

• IoT sensors


Supervised Learning

Data Labels

• Real humans

• Experts

• User feedback

• Hired “eyes and

ears”


NewAmazon SageMaker Ground Truth

Label machine learning training data easily and accurately

Launched 11/28


Ground Truth: Data labeling tasks

Bounding boxes Image classification Semantic segmentation

Text classification Custom tasks


Labeled datasets

Active learning and auto data labeling

Machine Learning

Models

Input datasets

Active

Learning

Auto

Labeling

Label

Consolidation

Human

Labeling

Machine Learning

Models


Human workforce options

Public

On-demand, 24 x7 workforce

500,000+ independent

contractors worldwide,

powered by Amazon Mechanical Turk

Private

You source your own workers,

e.g. employees or contractors.

for data that needs to stay

within your organization

Vendors

Curated list of

third-party vendors,

specialized in labeling services,

available via the AWS Marketplace


Supervised Learning

Data Labels

X0,000

or more


Supervised Learning

Data Labels

ML

Algorithm


Supervised Learning

Data Labels

ML

Algorithm

ML

Model


Supervised Learning

Data Labels

ML

Algorithm

ML

Model

New

DataPrediction


Checklist



3. Collect training data from your data lake

4. Collect labels for your training data• From historical data

• From operations

• From real humans




Analysis



Data Integration

Data Preparation &

Cleaning

Feature Engineering

Model Training &

Parameter Tuning

Model Evaluation

Are

Business

Goals

met?

Model Deployment

Monitoring &

Debugging

– Predictions

YesNo

Da

ta A

ug

me

nta

tio

n

Fe

atu

re

Au

gm

en

tati

on

Re-training



Amazon SageMaker: Build, train, and deploy ML models at scale

Collect and

prepare training

data

Choose and

optimize your

ML algorithm

123

Set up and

manage

environments

for training

Train and

Tune ML Models Deploy models

in production

Scale and manage

the production

environment


Checklist




4. Collect labels for your training data

5. Build, train and deploy your ML model withAmazon SageMaker


Checklist





5. Build, train and deploy your ML model withAmazon SageMaker…or use a ready-to-run Amazon ML application service


Checklist





5. Build, train and deploy your ML model withAmazon SageMaker…or use a ready-to-run Amazon ML application service

6. Repeat from step 2


When should you consider using ML for a problem?

• Software too complex

• Manual processnot cost effective

• Lots of training dataavailable

• Easy to express in ML terms

Image: Unsplash


When is ML probably not a good idea?

• No data

• No labels

• Not a lot of time

• No tolerance for mistakes

Image: Unsplash


Summary

• Understand your business model

• Find bottlenecks, then ask:• Where can you add value with ML?

• Where can you reduce waste with ML?

• Build your data lake

• Be competent with data

• Build your first ML solution• Leverage AWS managed ML services

• Use Amazon SageMaker for building your own

• Iterate!


Related breakouts

From an Engineering Company to a Core ML Competency CompanyRaul-Andrei Firu, Haufe Lexware Group, Wed., Feb. 27th, Hall 6

How to Innovate Your Software with ML?4FriendsOnly.com, Can Do, fme and Haufe Group, Wed., Feb. 27th, Hall 4

Apache MXNet (Incubating) and Gluon: What's in it for You?Constantin Gonzalez, AWS, Wed., Feb. 27th, Keynote Hall


What will you build?





What will you build?





The Machine Learning process (technical view)

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



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



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



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

Easily build, train, and deploy machine learning models


Amazon SageMaker

Pre-built

notebooks for

common

problems

K-Means Clustering

Principal Component Analysis

Neural Topic Modelling

Factorization Machines

Linear Learner – Regression

DeepAR

Random Cut Forest

XGBoost

Latent Dirichlet Allocation

Image Classification

Seq2Seq

Linear Learner – Classification

BlazingText

ALGORITHMS

Apache MXNet

TensorFlow

Caffe2, CNTK,

PyTorch, Torch

FRAMEWORKSSe t up a nd m a nage

e nv i ronments fo r

t ra in ing

Tra in a nd t une

m o del ( t r i a l a nd

e r ror )

De p loy m o del

in p ro duc t ion

Sc a le a nd m a nage t he

p ro duc t ion e nv i ronment

Built-in, high-

performance

algorithms

Build


Amazon SageMaker

Pre-built

notebooks for

common

problems

Built-in, high-

performance

algorithms

One-click

training

Hyperparameter

optimization

Build Train

Deploy model

in productionScale and manage

the production

environment


Amazon SageMaker

Fully managed

hosting with auto-

scaling

One-click

deployment

Deploy

Pre-built

notebooks for

common

problems

Built-in, high-

performance

algorithms

One-click

training

Hyperparameter

optimization

Build Train

Documents

S U M M I T - Amazon Web Services...“We use Amazon Rekognition to enrich our mapping content. Rekognition’sText in Image allows us to continually update signage information so