Re cap2018

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS re:Invent re:Cap 2018
Ric Harvey
Sr. Technical Evangelist
Amazon Web Services
@ric__harvey
rjh@amazon.com

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2018

Firstly, State of the AWS Cloud
4 6 %
Y/Y G r ow t h
Q3 2018 vs Q3 2017
$2 7 B
Revenue run rate
ANNUALIZED FROM Q3 2018

The broadest and deepest cloud
platform for today’s builders

The broadest and deepest cloud platform

Amazon Global Network
• Redundant 100GbE network
• Redundant private capacity
between all Regions except China
Over 150 Global CloudFront PoPs
89 Direct Connect Locations
a e
o
q
i
h
Paris
Sweden
AWS GovCloud East
First 5 years: 4 regions
2016–2020: 13 regions
Next 5 years: 7 regions
A W S
REGIONAL
EXPANSION
1 9 R e g i o n s 5 7 A Z s
d
m
c
g
b
n
s
k
v
i
i
i
i i
i
i
i
Milan
i
Cape Town

AWS Global Accelerator: Before and After

#1 Security Priority on AWS Cloud

Manage with your
existing VMware tools
Seamlessly
migrate workloads
Run the same VMware
software on AWS that you
run in your data centers
VMware Cloud
on AWS

Momentum for VMware Cloud on AWS

N E W !
AWS coming to a data
center near you in two ways
AWS Outposts
VMware Cloud
on AWS
AWS
Native
AWS DESIGNED HARDWARE
The same that we run in our own
data centers
OPTION 1 OPTION 2

Family of hybrid options on AWS
Integrate your on-
premises resources with
AWS
Use your VMware
software and tools to
run workloads on
AWS
Run latency-sensitive
workloads on-premises
in a way that seamlessly
integrates with AWS
Run compute on the edge
in locations with limited
connectivity
AWS Storage
Gateway
Amazon VPC
AWS Direct
Connect
AWS Identity
and Access
Management
AWS
Directory
Service
AWS
OpsWorks
AWS
CodeDeplo
y
VMware Cloud
on AWS
AWS Outposts
Snowball Edge
Compute-
Optimized
AWS Systems
Manager

Computing - Instances

EC2 Instance Types
Burstable
T 3
Big Data
Optimized
H 1
Memory
Optimized
R 5
High
I/O
I 3
Compute
Intensive
C 5
Graphics
Intensive
G 3
General
Purpose
GPU
P 3
Memory
Intensive
X 1
X 1 e
General
Purpose
M 5
V i r t u a l
P r i v a t e
S e r v e r s
Bare Metal
High I/O
I 3 m
Dense
Storage
D 2 F 1
FPGA
A m a z o n
L i g h t s a i l
High-Memory
Intensive
Z 1
Powered by
M 5 a R 5 a
• Choose between processors on AWS general
purpose and memory optimized instances
• 10% lower prices on AMD-based instances
• Most applications can run on AMD-based variants
with little to no modification
M 5 d R 5 d C 5 dZ 1 d
• NVMe-based SSD block level instance storage physically
connected to the host server
• High-speed, low latency local block storage

N E W !
Up to 45% lower cost for scale-out workloads

Network
performance

High performance computing (HPC)

N E W !
Add-on capability Scale to thousands of cores Native integration

Computing - Containers

Miroservices to Service Mesh
Service Mesh Plane (Routing, Monitoring, Security)
Svc
A
Svc
B
Side
Car
Side
Car

N E W !
App Mesh with Amazon ECS
and Amazon EKS to better run containerized microservices at scale
Microservice A
Microservice B
Microservice C
Microservice A
Microservice B
Microservice C
Microservice C
Microservice C
Before After

N E W !
Easily create and maintain custom maps of your applications
Before
Version 2
After
Version 2

Running Containers & Serverless at scale
Different security
boundaries than
instances
Inefficient resource
utilization

Firecracker Architecture and Benefits
• Firecracker microVMs have the same security as KVM VMs
• Designed for low overhead, high density, and fast start times
• Built-in fair sharing

Computing - Serverless

IDEs Languages WorkflowsProgramming
Model
Everyone wants to just focus
on business logic in Serverless

N E W !
GE N E R AL L Y AVAI L AB L E
A W S
C l o u d 9
AWS Toolkit
for PyCharm
AWS Toolkit
for IntelliJ
AWS Toolkit
for VS Code
GENERALLY AVAILABLE IN DEVELOPER PREVIEW IN DEVELOPER PREVIEW
Open source toolkits meeting you where and how you like to work
AWS Toolkits for popular IDEs
+
IDEs

N E W !
Languages
Lambda
support
for Ruby
+ Bring any Linux compatible language
runtime;
Powered by new Runtime API - Codifies
the runtime calling conventions and
integration points
Same technology powering Ruby
support in AWS Lambda
Bring any Linux compatible
language runtime
Custom Runtimes
+
AWSOPENSOURCE
o f f e r e d b y
o f f e r e d b y
o f f e r e d b y
o f f e r e d b y
PARTNERSUPPORTED

N E W !
Extend the Lambda execution
environment with any binaries,
dependencies, or runtimes
Lambda Layers
BUSINESS
LOGIC
LIB
A
LIB
B
BUSINESS
LOGIC
LIB
A
LIB
B
BUSINESS
LOGIC
LIB
A
LIB
B
BUSINESS
LOGIC
LIB
A
LIB
B
Programming
Model
Before
BUSINESS
LOGIC
BUSINESS
LOGIC
BUSINESS
LOGIC
BUSINESS
LOGIC
LIB
A
LIB
B
After

N E W !
Programming
Model
Store, share, and deploy
serverless applications
Serverless
Application
Repository
Compose application architectures
from reusable building blocks
Nested Applications
using Serverless
Application Repository
Deploy new architectures as a set of
serverless apps (nesting)
Foster best organizational practices and
reduce duplication of effort
Share components, modules and full
applications privately with teams or
publicly with others to improve agility
+ + Integrate Lambda
functions into existing
web architectures
ALB Support
for Lambda
AWS
Lambda
Applicatio
n Load
Balancer
AWS
Fargate
Amazon
EC2

N E W !
MOBILE APPS
CHAT
DASHBOARDS
IoT DEVICES Amazon API
Gateway
WebSockets API
LAMBDA
FUNCTIONS
PUBLIC ENDPOINTS
ON AMAZON EC2
AMAZON
KINESIS
ANY OTHER AWS
SERVICE
Stateful connection
A L L P U B L I C L Y
A C C E S S I B L E
E N D P O I N T S
Stateful connection
Programming
Model
This new type of API will enable customers
to build real-time two way communication
applications backed by Lambda functions
or other API Gateway integrations.
Web Socket support
for API Gateway

N E W !
WorkFlow
Step Functions
Process photo
Resize
image
Extract
metadata
Facial
recognition
Load in Database
P A R A L L E L S T E P S
Start
E n d
Glue AWS services together
without writing code
Step Functions
API Connectors
Amazon
ECS
AWS
Fargate
Amazon
DynamoD
B
Amazon
SNS
AWS
Batch
Amazon
SQS
AWS Glue
Amazon
SageMak
er
+

N E W !
Fully managed, highly-
available Kafka clusters
Highly available with rolling upgrades
Fully compatible – Run your
Kafka applications with zero
code changes
Fully managed and highly
available Apache Kafka service
Amazon Managed
Streaming for Kafka
WorkFlow

IDEs Languages
AWS Cloud9
AWS Toolkit for
PyCharm
AWS Toolkit for
IntelliJ
AWS Toolkit for
VS Code
AWS Lambda layers
AWS Nested apps
Websocket support for
API Gateway
ALB support for
Lambda
Workflows
Programming
Model
Step Functions API
Connectors
Amazon Managed
Streaming for Kafka
Build increasingly powerful applications, faster

This is the moment for #databasefreedom

Aurora is the foundation for cloud native Database
Amazon
Aurora
NEW
Global Database

Provisioning capacity for DynamoDB
( S o m e t i m e s i t ’ s h a r d t o k n o w w h a t ’ s b e s t )
HIGH-SCALE APPLICATIONS
Estimating how much throughput
capacity to provision can be guesswork
Not enough experience with app can
cause unexpected extreme app usage
Spikey traffic can be costly to maintain
availability and performance
Auto-scaling can cause lag time apps
can’t afford

N E W !
Amazon DynamoDB
Read/Write Capacity On Demand
N o m o r e c a p a c i t y p l a n n i n g – p a y o n l y f o r w h a t y o u u s e
No capacity planning
No need to specify how much read/write
throughput you expect to use
Pay only for what you use
Pay-per-request pricing
Ideal for unpredictable workloads
Ramp from zero to tens of thousands of
requests per second on demand

Existing time-series databasesRelational databases
Difficult to scale
Manual effort needed for enterprise-grade
availability and reliability
Limited data lifecycle management capabilities
Unnatural for time-series data
Rigid schema inflexible for fast-moving time-
series data
Building with time-series
data is challenging
Lack time-series analytic functions like
smoothing, approximation, and interpolation
1
2
3
Clickstream data
IoT Sensor Readings
DevOps data

N E W !
Timestream is 1,000X faster and
1/10th the cost of relational databases
Trillions of daily events
Serverless
Time-series analytics (interpolation,
smoothing, approximation) built in
Multiple orders of magnitude
improvement in query performance
Fast, scalable, fully managed
time series database
Amazon
Timestream

How do we think about Blockchain?

TRANSACTIONS WITH
DECENTRALIZED
TRUST
2

DMV
Track vehicle
title history
Manufacturers
Track distribution of a
recalled product
HR & Payroll
Track changes to an
individual’s profile
Healthcare
Verify and track hospital
equipment inventory
Common customer use cases on Blockchain
Relational
databases
Blockchain
frameworks
Using sub-optimal ways
to solve the problem
LEDGERS WITH
CENTRALIZED TRUST
1

Mortgage Lenders
Processing syndicated loans
Small Businesses
Transact with suppliers and
distributers
Retail
Streamline customer
rewards
Financial
Institutions
Peer-to-peer payments
Blockchain Frameworks
Hard to scaleSet up is hard
Complicated
to manage
Expensive
TRANSACTIONS WITH
DECENTRALIZED
TRUST
2LEDGERS WITH
CENTRALIZED TRUST
1
Common customer use cases on Blockchain

N E W !
Cryptographically Verifiable
All changes are cryptographically chained and
verifiable
Transparent
Full visibility into entire data lineage
Immutable
Append-only, immutable journal
tracks history of all changes
Highly scalable
Automatically scale up or down
Easy to use
Query with familiar SQL operators
Fast
Execute 2-3X more transactions
Fully managed ledger database that provides a transparent, immutable,
cryptographically verifiable transaction log owned by a central trusted authority
Amazon Quantum Ledger Database

N E W !
Create and manage scalable blockchain networks
Amazon Managed Blockchain
Choose Hyperledger Fabric or
Ethereum
Create blockchain networks
with a few clicks; manage them
with simple API calls
Scales to support thousands of
applications running millions of
transactions
Easy to move data into QLDB for
further analysis

Storage Options

N E W !
Amazon
EFS IA
Amazon S3
Intelligent-Tiering
AWS
DataSync
AWS Transfer for
SFTP

N E W !
Amazon Glacier Deep Archive
No tape to
manage
$0.00099/GB/month
Less than 1/4th the cost of
Glacier
Designed for
99.999999999
durability
Recover data in
hours vs.
days/weeks
L o w e s t c o s t s t o r a g e a v a i l a b l e i n t h e c l o u d … e v e n l o w e r
t h a n o n - p r e m i s e s t a p e

Set up
Storage
1
Move
data
2
Cleanse
and prep
data
3
Configure and enforce
security and compliance
policies
4
Make data
accessible for
analytics
5
Steps for building and managing a data lake

N E W !
Enforce security policies across
multiple services
Gain and manage new insights
Move, store, catalog, and clean your
data faster with machine learning
A service that allows you to build a secure data lake in days
Amazon Lake Formation

Worldwide Windows
Public Cloud IaaS
Instances by
Cloud Provider

N E W !
Amazon FSx
for Windows
File Server
Windows native for fully compatible Windows File
System experience
No hardware or software to manage
Secure and compliant including PCI-DSS,
ISO, and HIPAA
Up to 10s of GB/s throughput with sub-
millisecond latencies
(Compatibility with AD, Windows access control, and native
Windows Explorer experience)
Fully managed Windows file system
built on native Windows file servers

N E W !
High throughput, low latency –
100s of GBs/s and millions of
IOPS
Seamless integration with
Amazon S3
Secure and compliant including
PCI-DSS, ISO and HIPAA
Fully managed file system for high
compute intensive workloads
Amazon FSx
for Lustre

“I want it all, and I want it now.”

More Machine Learning is happening on AWS than anywhere else
10,000s
of active customers
2xcustomer references

More Machine Learning is happening
on AWS than anywhere else

Machine Learning on AWS to All Developers

FRAMEWORKS
INFRASTRUCTURE
P3 P3dn
INTERFACES
Infrastructure and frameworks for Machine Learning
C O M I N G
S O O N
Scale model training
performance across multiple
instances
100 Gbps networking
64 GB GPU memory
768 RAM (System Memory)
P3dn.24xl
3X faster network throughput than
any other provider
2X as much GPU memory than any other GPU
instance from other major providers
100+ GB more system memory than the next largest
instance available from other providers
“In the cloud, 85% of TensorFlow
workloads run on AWS"
Nucleus Research, TensorFlow on AWS, 28 November 2018,
Analyst: Rebecca Wettemann

Fastest training
times for
TensorFlow
30m
training time
(Mountain View)
ResNet50 only
Specialized hardware
only available in beta
14m
training time
(Seattle)
ResNet50, convolutional neural
networks (for images), recurrent
neural networks (for language
recommendations
Optimized for P3 with
global availability
F A S T E S T
T R A I N I N G
T I M E
Scaling
TensorFlow
65%
STOCK
TENSORFLOW
scaling efficiency
with 256 GPUs
90%
AWS-OPTIMIZED
TENSORFLOW
scaling efficiency
with 256 GPUs
NEW
Thinking TensorFlow on Cloud

Running inference in production
drives the majority of cost
for Machine Learning
Inference
Training
Infrastructure costs
But what about
inference?
It’s never been easier, faster, or more cost-
effective to train Machine Learning models

Two main drivers of inference
inefficiency: complexity and cost of
Machine Learning inference today
Elasticity is
important
One size does
not fit all

N E W !
EC2
Instance
EC2
Instance
EC2
Instance
GPU
Add GPU acceleration to any Amazon EC2 instance for
faster inference at much lower cost (up to 75% savings)
Amazon
Elastic Inference
P 3 . 8 X L
P 3 P 3
P 3 P 3
Amazon
Elastic Inference
36 TOPS GPU
M5.large
Amazon
Elastic Inference

Starting at
1 TFLOPS
Any instance
family
Simple speech and
language models
Up to
32 TFLOPS
Recommendation engines
or fraud detection models
Provision Elastic
Inference capacity
inside VPC
360,000ResNet-50
Computer vision
deep learning model
images per hour,
inference
$0.22
per hour
on medium EI accelerator
75%
lower cost
L O W E S T C O S T
A V A I L A B L E
Elastic Inference - Scale & Cost

Workloads needing entire GPU
or that are latency sensitive

N E W !
High throughput
Low latency
Hundreds of TOPS
Multiple
data types
Multiple
ML Frameworks
INT8, FP16,
mixed precision
TensorFlow, MXNet,
PyTorch, Caffe2, ONNX
EC2 instances
Amazon SageMaker
Amazon Elastic Inference
High performance machine learning inference chip,
custom designed by AWS
AWS Inferentia

ML Services

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
Bringing Machine Learning to all developers
One-click
training
Set up and manage
environments
for training
Pre-built
notebooks for
common problems
Collect and prepare
training data
Built-in, high
performance
algorithms
Choose and optimize
your ML algorithm
Optimization
Train and tune
model(trial and error)
One-click
deployment
Deploy model
in production
Fully managed with auto-
scaling, health checks,
automatic handling of node
failures, and security checks
Scale and manage the
production environment

More than ten thousand customers using Amazon SageMaker

What else are customers asking us for, to give them
“a little less conversation,
a little more action, please”?

Successful models require
high-quality training data
Pre-built
notebooks for
common problems
Collect and prepare
training data

STATIONARY CAR
STATIONARY CAR
MOVING
CARPEDESTRIAN
STOP
SIGN
DIRECTION
SIGN
STOP
SIGN
TRAFFIC
SIGNALS
TRAFFIC
SIGNALS
STATIONARY
VEGETATION
STATIONARY ROAD
STATIONARY
BUILDING
MOVING
CAR
PAVEMENTSuccessful models require
high-quality training data
Pre-built
notebooks for
common problems
Collect and prepare
training data

N E W !
Human
annotations
Data in S3
Automatic
annotations
Training
data
Simple, pre-built workflows
Mechanical Turk
Your own employees
Third party vendors
Active
Learning
model
>80% confidence
<80% confidence
Build highly accurate training datasets and reduce data
labeling costs by up to 70% using Machine Learning
Amazon SageMaker - Ground Truth

Amazon SageMaker: broad set of built-in
algorithms
Designed to
be 10x faster
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner (Regression)
BlazingText
Reinforcement Learning
XGBoost
Topic Modeling (LDA)
Image Classification
Seq2Seq
Linear Learner (Classification)
DeepAR Forecasting
A L G O R I T H M S
Built into
Amazon SageMaker
Improving and
expanding continually
40% more algorithms added
since SageMaker launch

N E W !
Natural Language Processing
Computer Vision
Speech Recognition
Text Clustering
Text Generation
Text Classification
Grammar and Parsing
Named Entity Recognition
Text to Speech
Handwriting Recognition
Object Detection in Images
3D Images
Text OCR
Video Classification
Speaker Identification
RankingRegression
Anomaly Detection
Register with AWS Marketplace
Automatic validation on SageMaker
Package algorithm, models and configuration
Self-service listing on AWS Marketplace
Browse or search
AWS Marketplace
Subscribe
in a single click
Available through
Amazon SageMaker
Over a hundred algorithms and models that can
be deployed directly to Amazon SageMaker
AWS Marketplace
for Machine Learning
S e l l i n g a l g o r i t h m s &
m o d e l s o n A W S
M a r k e t p l a c e

SOPHISTICATION
OF ML MODELS
A M O U N T O F T R A I N I N G D A T A R E Q U I R E D
Reinforcement
Learning (RL)

How does Reinforcement Learning work?
Algorithm controls Pac-Man
Learn to play to get the highest score possible
Maximize rewards and minimize penalties
Learn advanced strategies

N E W !
Fully managed reinforcement
learning algorithms
TensorFlow, MXNet,
Intel Coach, and Ray RL
2D and 3D simulation
environments via OpenGym
Simulate environments
with Amazon Sumerian and
AWS RoboMakerExample notebooks
and tutorials
New machine learning capabilities in Amazon SageMaker
to build, train, and deploy with Reinforcement Learning
Amazon SageMaker RL

Can we help developers get rolling
with Reinforcement Learning?
(literally)

N E W !
Fully autonomous 1/18th scale race car,
driven by Reinforcement Learning
AVAILABLE FOR PRE-ORDER ON AMAZON.COM
AWS DeepRacer

HD Video Camera
Gyroscope
for direction and orientation
Accelerometer
for measuring change in speed
Two batteries:
one to power on-board
compute, one to drive
motorsDual-core
Intel Atom® Processor
Introducing AWS DeepRacer
All wheel drive,
monster truck chassis
Suspension
mounted high for a view of the road
Both accelerometer and gyroscope useful in the
future for building more sophisticated models such
as finding the perfect racing line or path finding

AWS DeepRacer: How does it work?
3D simulator with
virtual car and track
Rewards RL algorithm

Build reinforcement learning model
DeepRacer League Races at AWS Summits
Winners of each DRL Race and top points getters
compete in Championship Cup at re:Invent 2019
Virtual tournaments through the year
AWS DeepRacer League
World’s first global autonomous
racing league, open to anyone

AI Services

No master algorithm
for personalization
and recommendation
Music
recommendation
Tracks, artists, albums
Film
recommendation
Actors, directors, genres
Product
recommendation
Pricing, category, offers
Article
recommendation
Themes, geography,
breaking news

N E W !
Activity stream
from app
Views, signups,
conversion, etc.
Inventory
Articles,
products, videos,
etc.
Demographics
(optional)
LOAD DATA
(EMR Cluster)
INSPECT
DATA
IDENTIFY
FEATURES
SELECT
ALGORITHMS
SELECT
HYPERPARAMETERS
TRAIN
MODELS
OPTIMIZE
MODELS
HOST
MODELS
BUILD FEATURE
STORE
CREATE
REAL-TIME
CACHES
Customized
personalization &
recommendation
API
F u l l y m a n a g e d b y A m a z o n P e r s o n a l i z e
Amazon Personalize
Age, location, etc.
Real-time personalization and recommendation service,
based on the same technology used at Amazon.com
Amazon Personalize

The perils of
poor predictions
in forecasting
TIME
SALES
FORECAST
SALES

N E W !
Amazon Forecast
Any historical
time-series
Integrates with SAP and
Oracle Supply Chain
Custom forecasts
with 3 clicks
50% more
accurate
1/10th
the cost
Integrates with
Amazon Timestream
Retail demand Travel demand AWS usage
Revenue forecasts Web traffic Advertising demand
Generate forecasts for:
Accurate time-series forecasting service, based on the same technology
used at Amazon.com

Historical data
Supply chain,
inventory, etc.
Customized
forecasting API
Inspect
data
Identify
features
Select
from 8
algorithms
Select
Hyperparameters
Host
models
Load
data
Train
models
Optimize
models
Related “causal” data
Weather, special offers,
product details
F u l l y m a n a g e d b y
A m a z o n F o r e c a s t
Amazon Forecast
Amazon Forecast: Machine Learning time-series forecasting

Dealing with documents is demanding
Manual data entry
Rules and template-based
extraction
Optical Character
Recognition (OCR)
Identify documents in any format
Extract text from those documents, accurately
Document formats are variable and changes unexpectedly

N E W !
OCR++ service to easily extract text and data from virtually any document
Amazon Textract

Integrate ML to AWS Services

AWS IoT – Broad and Deep Integrations with Cloud
End-point Gateway
Edge Network AWS Cloud
Secure Device
Connection &
Messaging
Fleet on-boarding,
Softeare Update
Fleet Audit&
Security
Data Analytics &
Intelligence
Easily collect and
organize data from
industrial equipment
NEW
Easily detect and
respond to events
from IoT sensors
and applications
NEW
Visually
develop IoT
applications
NEW

Robotic development is difficult and time consuming
Requires machine learning expertise
for intelligent functions
Many
prototyping iterations
Days spent setting
up and configuring
Months to building a realistic
simulation environment
Duplicated efforts integrating an
application management system

N E W !
Easily develop, test, and deploy intelligent robotics applications
AWS RoboMaker
Development
Environment
SimulationCloud Extensions for
ROS
Fleet
Management

How to do Space Business?
• Build ground stations, sign long-term leases, and purchase
excess bandwidth
• Expensive to build and difficult to maintain and require
high CAPEX investment to scale, and experience network
latency and scheduling conflicts
• Have opaque investing and pricing for startups

N E W !
Easily control satellites and ingest data with
fully managed Ground Station as a Service
AWS Ground Station
Low Earth Orbit (LEO)
Medium Earth Orbit (MEO)
Simultaneous narrowband
S-band, X-band and UHF
downlink
Receive satellite data into
Amazon VPC and Process
data in AWS Cloud

So, Are you Well-Architected?

Well-Architected
started
2012 2013
AWS SA reviews
2014
Questions
across 4 pillars
2015
Published Framework
20142016
Operational
Excellence
2017
Serverless Lens
2018
IoT Lens
HPC Lens
APN Partners
https://aws.amazon.com/well-architected
PILLAR-SPECIFIC
WHITEPAPERS, LENS
AWS WELL-ARCHITECTED
FRAMEWORK WHITEPAPER
FREE ONLINE TRAINING

SECURITY
2
OPERATIONAL
EXCELLENCE
1
RELIABILITY
3
COST
OPTIMIZATION
5
PERFORMANCE
EFFICIENCY
4
Reviews + ( Solutions
Architect
APN
Partner
OR
)Your team
TECHNICAL & BUSINESS
LEADS

N E W !
AWS Well-
Architected Tool
Measure and improve your architecture using
AWS Well-Architected best practices
Implement workplans to improve
your architecture
Stay up to date as your
architecture evolves
Review workloads against
best practices

Here is all of new launches!
https://aws.amazon.com/new/reinvent

Join the conversation
DM @ric__Harvey on Twitter
and send me your email address
for an invite

Go Build!
Here to help you build

Thank you!
Ric Harvey
Sr. Technical Evangelist
Amazon Web Services
@ric__harvey
rjh@amazon.com

Re cap2018

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