AWS re:Invent 2017 Recap Webinar Summary

©2017, Amazon Web Services, Inc. or its affiliates. All rights reserved
Ivan Cheng ( )
AWS Solutions Architect
AWS re:Invent 2017 Recap Webinar
25th January, 2018

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B U I L D E R S MU S I C I A N S

CORE SERVICES
Integrated Networking
Rules Engine
Device Shadows
Device SDKs
Device Gateway
Registry
Local Compute
Custom Model
Training & Hosting
Conversational Chatbots
Virtual Desktops
App Streaming
Schema Conversion
Image & Scene
Recognition
Sharing &
Collaboration
Exabyte-Scale
Data Migration
Text to Speech
Corporate Email Application Migration
Database Migration
Regions
Availability Zones
Points of Presence
Data Warehousing
Business Intelligence
Elasticsearch
Hadoop/Spark
Data Pipelines
Streaming Data
Collection
ETL
Streaming Data
Analysis
Interactive SQL
Queries
Queuing & Notifications
Workflow
Email
Transcoding
Deep Learning
(Apache MXNet,
TensorFlow, & others)
Server MigrationCommunications
MARKETPLACE
Business Apps Business Intelligence DevOps Tools Security Networking StorageDatabases
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API Gateway
Single Integrated
Console
Identity
Sync
Mobile Analytics
Mobile App Testing
Targeted Push
Notifications
One-click App
Deployment
DevOps Resource
Management
Application Lifecycle
Management
Containers
Triggers
Resource Templates
Build & Test
Analyze & Debug
Identity
Management
Key Management
& Storage
Monitoring &
Logs
Configuration
Compliance
Web Application
Firewall
Assessment
& Reporting
Resource & Usage
Auditing
Access Control
Account
Grouping
DDOS
Protection
TECHNICAL & BUSINESS SUPPORT
Support
Professional
Services
Optimization
Guidance
Partner
Ecosystem
Training &
Certification
Solutions Management Account Management Security & Billing
Reports
Personalized
Dashboard
Monitoring
Manage
Resources
Data Integration
Integrated Identity &
Access
Integrated Resource &
Deployment Management
Integrated Devices
& Edge Systems
Resource
Templates
Configuration
Tracking
Server
Management
Service
Catalogue
Search
MIGRATIONHYBRID ARCHITECTUREENTERPRISE APPSMACHINE LEARNINGIoTMOBILE SERVICESDEV OPSANALYTICS
APP SERVICES
INFRASTRUCTURE SECURITY & COMPLIANCE MANAGEMENT TOOLS
Compute
VMs, Auto-scaling, Load
Balancing, Containers,
Virtual Private Servers,
Batch Computing, Cloud
Functions, Elastic GPUs,
Edge Computing
Storage
Object, Blocks, File, Archivals,
Import/Export, Exabyte-scale
data transfer
CDN
Databases
Relational, NoSQL,
Caching, Migration,
PostgreSQL compatible
Networking
VPC, DX, DNS
Facial Recognition &
Analysis
Facial Search
Patching
Contact Center
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M O S T R O B U S T , F U L L Y F E A T U R E D T E C H N O L O G Y I N F R A S T R U C T U R E P L A T F O R M

516
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159
280
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LAUNCHES
2008 2009 2010 2011 2012 2013 2014 2015 2016
1,300+
2017
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CONTAINERS
C O M P U T E

AMAZON ELASTIC CONTAINER SERVICE (ECS)
The easiest way to deploy and manage containers

Service integrations are at
the container level
Scales to support clusters
and applications of any size
Integration with entire
AWS platform
1 2 3
ALB, Auto Scaling, Batch, Elastic Beanstalk,
CloudFormation, CloudTrail, CloudWatch Events,
CloudWatch Logs, CloudWatch Metrics, ECR, EC2 Spot,
IAM, NLB, Parameter Store, and VPC
Why customers love ECS
AMAZON ELASTIC CONTAINER SERVICE (ECS)
The easiest way to deploy and manage containers

W H AT A B O U T K U B E R N E T E S ?

Managed Kubernetes on AWS
Amazon Elastic Container
Service for Kubernetes (EKS)
Available in preview today
NEW!

A M A Z O N E L A S T I C C O N T A I N E R S E R V I C E F O R K U B E R N E T E S ( E K S )
Hybrid cloud
compatible
Highly
available
Automated
upgrades and
patches
Integrated with
AWS Services
CloudTrail, CloudWatch, ELB,
IAM, VPC, PrivateLink

… B U T W H A T E L S E ?
M A N A G E D C L U S T E R S A R E G R E A T …

Run containers without managing
servers or clusters
AWS Fargate
Available for ECS today
Available for EKS in 2018
NEW!

A W S F A R G A T E
No clusters
to manage
Manages underlying
infrastructure
Easy to run,
easy to scale
Run containers on ECS and EKS without managing servers

C O M P U T E
SERVERLESS

C U S T O M E R S L O V E L A M B D A

A W S L A M B D A I S E V E R Y W H E R E
Event-driven services Event sources Lambda inside
AWS Lambda Amazon S3 Amazon CloudFormation AWS IoT
Amazon API Gateway Amazon DynamoDB Amazon CloudWatch Logs AWS IoT Button
AWS Step Functions Amazon Kinesis Streams Amazon CloudWatch Events AWS Greengrass
AWS X-Ray Amazon Kinesis Firehose AWS CodeCommit AWS Snowball Edge
Amazon SNS AWS Config AWS Lambda@Edge
Amazon SES Amazon Lex
Amazon Cognito Amazon CloudFront
AWS IoT
AWS Lambda

E V E R Y T H I N G I S E V E R Y T H I N G
AWS
CodeDeploy
AWS
CloudTrail
AWS Config
and Config
Rules
AWS IAM
AWS
PrivateLink
Managed NAT
Gateway
VMware Cloud
on AWS
Group
Resource
Tagging

C U S T O M E R S A R E M O V I N G T O O P E N D A T A B A S E S

AMAZON AURORA
MySQL and PostgreSQL compatible
Several times faster than standard MySQL and PostgreSQL
Highly available and durable
1/10th the cost of commercial grade databases
Fastest-growing AWS
service, ever

re:Invent 2015: Thousands of customers
A U R O R A I S T H E F A S T E S T G R O W I N G S E R V I C E
I N T H E H I S T O R Y O F A W S

re:Invent 2016: 3.5X more customers

Today: Tens of thousands of customers

A U R O R A T O D A Y :
S C A L E O U T F O R M I L L I O N S O F R E A D S P E R S E C O N D
Seamless recovery from read replica
failures
Auto-scale new read replicas
Up to 15 read replicas across 3 availability zones
Application
Read Replica
1
Master
Node
Read Replica
2
Shared Distributed Storage Volume
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3

Scale out for both reads and writes
A u r o r a M u l t i - M a s t e r
Sign up for the preview today
NEW!

Application
Read/Write
Master 2
Read/Write
Master 1
Shared Distributed Storage Volume
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3
Read/Write
Master 3
A U R O R A M U L T I - M A S T E R
First relational database service with scale-out across multiple datacenters
Zero application downtime from ANY node failure
Zero application downtime from ANY AZ failure
Multi-region coming in 2018
Faster write performance

U N P R E D I C T A B L E W O R K L O A D S A R E C H A L L E N G I N G
DATABASE
REQUESTS
TIME

On-demand, auto-scaling, serverless database
A u r o r a S e r v e r l e s s
Sign up for the preview today
NEW!

A U R O R A S E R V E R L E S S
Automatically
scales capacity up
and down
Pay per second and
only for the database
capacity you use
Starts up on demand
and shuts down when
not in use
On-demand, auto-scaling database for applications with unpredictable or cyclical workloads
No need to
provision instances

E V O L U T I O N O F D A T A B A S E S
AU ROR A
Amazon RDS
C OMMER C IAL C OMMU N ITY
R e l a t i o n a l d a t a b a s e s N o n - r e l a t i o n a l d a t a b a s e s
Amazon
DynamoDB
KEY VALUE
DOCUMENT
Amazon
ElastiCache
IN-MEMORY STORE

Fully managed, multi-master, multi-region database
DynamoDB
Global Tables
Generally available today
NEW!

Build high performance, globally
distributed applications
Low latency reads and writes to
locally available tables
Disaster proof with multi-region
redundancy
Easy to setup and no application
re-writes required
D Y N A M O D B G L O B A L T A B L E S
First fully managed, multi-master, multi-region database

WHAT ABOUT BACKUPS?

Only cloud database to provide on-demand and continuous backups
NEW!
Backup hundreds of
TB instantaneously
with NO
performance impact
On-Demand Backups
for long term data
archival and
compliance
Point In Time Restore
for short term retention
and protection against
application errors
On-Demand Backup generally available today
Point In Time Restore coming early 2018
I N T R O D U C I N G D Y N A M O D B B A C K U P A N D R E S T O R E

H I G H L Y C O N N E C T E D D A T A
Social news feed Restaurant recommendations Retail fraud detection

C H A L L E N G E S B U I L D I N G A P P S W I T H H I G H L Y C O N N E C T E D D A T A
Difficult to
maintain
high availability
Difficult to
scale
Relational databases
Existing graph databases
Limited
support for
open standards
Too
expensive
Unnatural for
querying graph
Inefficient
graph
processing
Rigid schema
inflexible for
changing graphs

Fully managed graph database
Amazon Neptune
NEW!

A M A Z O N N E P T U N E
FA S T A N D S C A L A B L E E A S Y
Build powerful queries
easily with Gremlin
and SPARQL
6 replicas of your data
across 3 AZs with full
backup and restore
R E L I A B L E
Supports Apache
TinkerPopTM and W3C
RDF graph models
O P E N
Fully managed graph database
Store billions of
relationships and
query with milliseconds
latency

E V O L U T I O N O F D A T A B A S E S
Amazo n Ne ptun e
GRAPH
Amazo n Dynamo DB Amazo n ElastiCache
KEY VALUE
DOCUMENT
IN-MEMORY STORE
AUROR A
Amazon RDS
C OMMER C IAL C OMMU N ITY
R e l a t i o n a l d a t a b a s e s N o n - r e l a t i o n a l d a t a b a s e s

DATA LAKES

A M A Z O N S 3 I S T H E M O S T P O P U L A R C H O I C E F O R D A T A L A K E S
Most ways to
bring data in
Best security,
compliance, and
audit capabilities
Object-level
controls
Unmatched durability,
availability,
and scalability
Twice as many
partner
integrations
Business insights
into your data

M A K I N G P E T A B Y T E - S C A L E A N A L Y T I C S A C C E S S I B L E T O
C O M P A N I E S O F A L L S I Z E S
Amazon Redshift
+ Redshift Spectrum
Amazon
QuickSight
Amazon EMR
Hadoop, Spark, Presto,
Pig, Hive…19 total
Amazon
Athena
Amazon
Kinesis
Amazon
Elasticsearch Service
AWS Glue
S3 DATA LAKE

Objects in your S3 data lake
v v v v v v v v
v v v v v v v v
v v v v v v v
M O S T A N A L Y T I C S J O B S I N V O L V E P R O C E S S I N G
O N L Y A S U B S E T O F O B J E C T D A T A

New API to select and retrieve data
within objects
Accelerate any application that
processes a subset of object data in S3
Improve data access performance by
up to 400%
NEW!
v
Powerful new S3 capability to pull out only the object data you need using standard SQL expressions
I N T R O D U C I N G S 3 S E L E C T

CAN WE FURTHER EXTEND THE DATA LAKE?

I N T R O D U C I N G G L A C I E R S E L E C T
NEW!
Run queries on data stored at rest in Amazon Glacier
Any application can query Glacier data
Retrieve only what you need
Makes Glacier part of your data lake
Run queries directly on data stored in Glacier

M A C H I N E L E A R N I N G

A L O N G H E R I T A G E O F M A C H I N E L E A R N I N G A T A M A Z O N
Personalized
recommendations
Inventing entirely
new customer
experiences
Fulfillment
automation and
inventory
management
Drones Voice driven
interactions

F R A M E W O R K S A N D I N T E R F A C E S
AW S D E E P L E A R N I N G A P I
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch Theano Gluon

B O T T O M L A Y E R : F R A M E W O R K S A N D I N T E R F A C E S
P2
NVIDIA
Tesla V100
GPUs
P3
1 Petaflop of compute
NVLink 2.0
5,120 Tensor cores
128GB of memory
~14X faster than P2
P3 Instance AWS Deep Learning AMI

L I S T E N T O A N D I N V E N T F O R C U S T O M E R S
Frameworks
KERAS
Interfaces

ML IS STILL TOO COMPLICATED FOR EVERYDAY DEVELOPERS
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
A m azon SageM aker
NEW!

A M A Z O N S A G E M A K E R S O L V E S A L L O F T H E S E P R O B L E M S
Collect and prepare
training data
Choose and
optimize your ML
algorithm
Set up and manage
environments for
training
Deploy model
in production
Scale and manage
the production
environment
(trial and error)

A M A Z O N S A G E M A K E R
Pre-built
notebooks for
common
problems
BUILD
Choose and
optimize your ML
algorithm
Set up and manage
environments for
training
(trial and error)
Deploy model
in production
Scale and manage
the production
environment

Pre-built
notebooks for
common
problems
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner - Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner - Classification
ALGORITHMS
Apache MXNet
TensorFlow
Caffe2, CNTK,
PyTorch, Torch
FRAMEWORKS
Set up and manage
environments for
training
Train and tune
model (trial and
error)
Deploy model
in production
Scale and manage the
production environment
Built-in, high
performance
algorithms
BUILD

Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
BUILD TRAIN
(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
Hyperparameter
optimization
BUILD TRAIN
Deploy model
in production
Scale and manage
the production
environment

Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
deployment
One-click
training
Hyperparameter
optimization
Scale and manage
the production
environment
BUILD TRAIN DEPLOY

Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

CAN WE DO MORE TO PUT ML IN
THE HANDS OF ALL DEVELOPERS
(LITERALLY)?

AWS DeepLens
The world’s first wireless, deep learning
enabled video camera for developers
Av a i l a b l e o n a m a z o n . c o m n e x t y e a r
NEW!

W H A T I S A W S D E E P L E N S ?
HD video camera
Custom-designed
deep learning
inference engine
Micro-SD
Mini-HDMI
USB
USB
Reset
Audio out
Power
HD video camera
with on-board
compute optimized
for deep learning
Tutorials, examples,
demos, and pre-built
models
From unboxing to
first inference in
<10 minutes
Integrates with Amazon
SageMaker and AWS
Lambda
10
MIN

VISION LANGUAGE
A P P L I C A T I O N S E R V I C E S
Amazon Rekognition Amazon Polly Amazon Lex

Search, analyze, and organize millions of images
A M A Z O N R E K O G N I T I O N
Objects and scenes
Facial analysis and recognition
Inappropriate content detection
Celebrity recognition
Image in text recognition

Real-time and batch video analytics
Amazon
Rekognition Video
Generally available
today
NEW!

A M A Z O N R E K O G N I T I O N V I D E O
V I D E O
Video in. People, activities, and details out.
Objects and scenes
Facial analysis and recognition
Inappropriate content detection
Celebrity recognition
Person tracking

S T R E A M I N G D A T A F R O M C O N N E C T E D D E V I C E S

Securely ingest and store video, audio, and
other time-encoded data
Amazon Kinesis
Video Streams
NEW!

VISION LANGUAGE
A P P L I C A T I O N S E R V I C E S
Amazon Rekognition Image Amazon Polly Amazon LexAmazon Rekognition Video

Automatic speech recognition
Amazon Transcribe
NEW!

A M A Z O N T R A N S C R I B E
Support for
telephony audio
Timestamp
generation
Intelligent punctuation
and formatting
Recognize multiple
speakers
Custom
vocabulary
Multiple
languages
Automatic conversion of speech into accurate, grammatically correct text

Automatically translates text
between languages
Amazon Translate
NEW!

A M A Z O N T R A N S L A T E
Real-time translation Batch analysis Automatic language recognition Low cost

BUT WHAT DOES ALL THIS TEXT MEAN?

Fully managed natural language processing
Amazon
Comprehend
NEW!

Discover valuable insights from text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend
A M A Z O N C O M P R E H E N D

STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES
A M A Z O N C O M P R E H E N D
Amazon
Comprehend
Discover valuable insights from text

B R O A D E S T M L P L A T F O R M T H A T ’ S T H E E A S I E S T T O U S E W I T H
T H E M O S T C U S T O M E R S
DATA LAKE STORAGE
Amazon S3
SECURITY
Access Control
Encryption
COMPUTE
Powerful GPU and CPU Instances
ANALYTICS
Amazon Athena
Amazon Redshift and Redshift Spectrum
Amazon EMR
(Spark, Hive, Presto, Pig)
AWS Glue
Amazon Kinesis
Amazon QuickSight
Amazon Macie
AWS Organizations
Complementary Services

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
AWS ML Platform
DATA LAKE STORAGE
Amazon S3
SECURITY
Access Control
Encryption
COMPUTE
ANALYTICS
Amazon Athena
Amazon EMR
AWS Glue
Amazon Kinesis
Amazon QuickSight
Amazon Macie
AWS Organizations

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
AWS ML Platform AWS ML Customers
DATA LAKE STORAGE
Amazon S3
SECURITY
Access Control
Encryption
COMPUTE
ANALYTICS
Amazon Athena
Amazon EMR
AWS Glue
Amazon Kinesis
Amazon QuickSight
Amazon Macie
AWS Organizations

N e w S e r v i c e s a t a g l a n c e
C o m p u t e
- H 1 , M 5 , B a r e M e t a l E C 2
- E l a s t i c C o n t a i n e r
S e r v i c e s f o r K u b e r n e t e s
- A m a z o n F a r g a t e
D a t a b a s e
- A u r o r a M u l t i - M a s t e r
- D y n a m o D B G l o b a l T a b l e
- D y n a m o D B B a c k u p a n d
r e s t o r e
- A m a z o n N e p t u n e
A n a l y t i c s
- S 3 S e l e c t
- G l a c i e r S e l e c t
A I
- A m a z o n S a g e M a k e r
- A W S D e e p L e n s
- A m a z o n R e k o g n i t i o n V i d e o
- A m a z o n K i n e s i s V i d e o S t r e a m s
- 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

Remember to complete
your evaluations!
R e m e m b e r t o c o m p l e t e y o u r e v a l u a t i o n s !

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