AWS Re:Invent 2019 Re:Cap

1. Chris Fregly
Developer Advocate, AI & Machine Learning
Amazon Web Services @ San Francisco
@cfregly
Re:Invent December 2019
65,000 Attendees
3,000 Sessions
https://aws.amazon.com/new/reinvent/

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
https://aws.amazon.com/new/reinvent/

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
• Improving the Developer Experience
• Compute
• Storage
• AI/ML
• Database & Analytics
• Networking
• Security
• Extending AWS beyond the Region

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

7. Improving the Developer
Experience

8. L E A R N M O R E SVS401 - Optimizing your serverless applications
Provisioned Concurrency on AWS Lambda
New Feature
• Keeps functions initialized and hyper-ready, ensuring start
times stay in the milliseconds
• Builders have full control over when provisioned
concurrency is set
• No code changes are required to provision concurrency on
functions in production
• Can be combined with AWS Auto Scaling at launch
DRAFTServerless
General Availability – December 3

9. Achieve up to 67% cost reduction and 50% latency reduction compared
to REST APIs. HTTP APIs are also easier to configure than REST APIs,
allowing customers to focus more time on building applications.
Reduce application costs by
up to 67%
Reduce application latency by
up to 50%
Configure HTTP APIs easier
and faster than before
HTTP APIs for Amazon API Gateway
Introducing
DRAFTMobile Services
Preview – December 4
L E A R N M O R E
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)

10. AWS Step Functions Express Workflows
Introducing
Orchestrate AWS compute, database, and messaging services at rates
greater than 100,000 events/second, suitable for high-volume event
processing workloads such as IoT data ingestion, streaming data
processing and transformation.
DRAFTApp Integration
General Availability – December 3
L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions

11. Amazon EventBridge Schema Registry
Introducing
Store event structure - or schema - in a shared central location, so it’s
faster and easier to find the events you need. Generate code bindings
right in your IDE to represent an event as an object in code.
DRAFTApp Integration
Preview – December 3
LEARN MORE
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)

12. Amplify for iOS & Android
Introducing
DRAFTMobile Services
General Availability – December 3
Open source libraries and toolchain that enable mobile developers to
build scalable and secure cloud powered serverless applications.
L E A R N M O R E MOB317 - Speed up native mobile development with AWS Amplify

13. Amplify DataStore
New Feature
DRAFTMobile Services
General Availability – December 3
Multi-platform (iOS/Android/React Native/Web) on-device persistent
storage engine that automatically synchronizes data between
mobile/web apps and the cloud using GraphQL.
L E A R N M O R E MOB402: Build data-driven mobile and web apps with AWS AppSync

14. Compute

15. Amazon EC2 Inf1 Instances
Introducing
The fastest and lowest cost machine learning inference in the cloud
Featuring AWS Inferentia, the first custom ML chip designed by AWS
Inf1 delivers up to 3X higher throughput and up to 40% lower cost
per inference compared to GPU powered G4 instances
Compute
General Availability – December 3
L E A R N M O R E CMP324-R: Deliver high performance ML inference with AWS Inferentia
Natural language
processing
PersonalizationObject
detection
Speech
recognition
Image processing Fraud
detection

16. AWS Graviton2 Processor
Introducing
Enabling the best price/performance for your cloud workloads
Graviton1 Processor Graviton2 Processor
DRAFTCompute
Preview – December 3
L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton

17. AWS Graviton2 Based Instances
Introducing
Up to 40% better price-performance for general purpose, compute
intensive, and memory intensive workloads.
l
M6g C6g R6g
DRAFT
Built for: General-purpose
workloads such as application
servers, mid-size data stores, and
microservices
Instance storage option: M6gd
Built for: Compute intensive
applications such as HPC, video
encoding, gaming, and simulation
workloads
Instance storage option: C6gd
Built for: Memory intensive
workloads such as open-source
databases, or in-memory caches
Instance storage option: R6gd
Compute
Preview – December 3
L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton

18. Amazon Braket
Introducing
Fully managed service that makes it easy for scientists and developers to
explore and experiment with quantum computing.
DRAFTQuantum Technology
Preview – December 2
LEARN MORE CMP213: Introducing Quantum Computing with AWS

19. AWS Nitro Enclaves
Introducing
Create additional isolation to further protect highly sensitive data
within EC2 instances
Nitro Hypervisor
Instance A Enclave A Instance B
EC2 Host Additional isolation
within an EC2 instance
Isolation between EC2
instances in the same host
Local socket
connection
DRAFTCompute
Preview – December 3

20. AWS Compute Optimizer
Introducing
Identify optimal EC2 instances and Auto Scaling group with a ML-
powered recommendation engine. Integrated with AWS Organizations.
DRAFTManagement Tools
General Availability – December 3
LEARN MORE CMP323-R: Optimize performance and cost for your AWS compute

21. AWS Compute Optimizer

22. Receive lower rates
automatically. Easy to use
with recommendations in
AWS Cost Explorer
Significant
savings of up to 72%
Flexible across instance family,
size, OS, tenancy or AWS
Region; also applies to AWS
Fargate & soon to AWS
Lambda usage
Compute/Cost Management
LEARN MORE CMP210: Dive deep on Savings Plans
Announced – November 6
Simplify purchasing with a flexible pricing model that offers savings of
up to 72% on Amazon ECS, AWS Fargate & AWS Lambda usage
Savings Plans

23. DRAFTContainers
General Availability – December 3
LEARN MORE CON-326R - Running Kubernetes Applications on AWS Fargate
Introducing
The only way to run serverless Kubernetes containers securely,
reliably, and at scale
Amazon EKS for AWS Fargate

24. Spare capacity with savings
up to 70% off of Fargate
standard pricing
Improved scalability,
reduced operational cost to
run containers
Containers
New Features
Accelerating momentum for AWS container services

25. Build and maintain secure OS images more quickly & easily
Introducing
DRAFTCompute
General Availability – December 3
EC2 Image Builder

26. AWS License Manager - Simplified Windows &
SQL Server BYOL
New Feature
DRAFTCompute
General Availability – December 1
• Bring your eligible Windows and SQL BYOL
Licenses to AWS
• Leverage existing licensing investments to save
costs
• Automate ongoing management of EC2
Dedicated Hosts
Simplified Management
Elasticity of EC2 for
Dedicated Hosts
with AWS License
Manager Integration
(New)
Windows BYOL
• B
A
• L
• A
LEARN MORE
WIN201 - Leadership session: Five New Features of Microsoft and .NET on AWS
that you want to learn

27. Introducing
DRAFTCompute
General Availability – December 1
Helps customers upgrade
legacy applications to run
on newer, supported
versions of Windows Server
without any code changes
Future-proof Reduced risk Cost-effective
Improved security
posture on supported,
new OS
Isolate old runtimes
Compliance with
industry regulations
No application
refactoring or recoding
cost
No extended support
costs
Decouple from
underlying OS
Low risk of failure on
subsequent OS updates
Supports all OS version Reduced operating costs
AWS End-of-support Migration Program for
Windows Server

28. Storage

29. EBS Direct APIs for Snapshots
Introducing
A simple set of APIs that provide access to directly read EBS snapshot data, enabling backup providers
to achieve up to 70% faster backups for EBS volumes at lower costs.
Up to 70% faster
backup times
More granular recovery
point objectives (RPOs)
Lower cost backups
Storage
Easily track incremental
block changes on EBS
volumes to achieve:
General Availability – December 3

30. Amazon S3 Access Points
Introducing
Simplify managing data access at scale for applications using shared data
sets on Amazon S3. Easily create hundreds of access points per bucket,
each with a unique name and permissions customized for each application.
DRAFT
General Availability – December 3
Storage

31. AI & Machine Learning

32. Please fasten your seatbelts!

33. VISION SPEECH TEXT SEARCH NEW CHATBOTS PERSONALIZATION FORECASTING FRAUD NEW DEVELOPMENT NEW CONTACT CENTERS NEW
Amazon SageMaker Ground
Truth
Augmented
AI
SageMaker
Neo
Built-in
algorithms
SageMaker
Notebooks NEW
SageMaker
Experiments NEW
Model
tuning
SageMaker
Debugger NEW
SageMaker
Autopilot NEW
Model
hosting
SageMaker
Model Monitor NEW
Deep Learning
AMIs & Containers
GPUs &
CPUs
Elastic
Inference
Inferentia
(Inf1)
FPGA
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
AI SERVICES
ML SERVICES
ML FRAMEWORKS & INFRASTRUCTURE
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon Connect
SageMaker Studio IDE NEW
NEW
AWS Machine Learning stack
NEW

34. AI Services

35. Pre:Invent highlights
https://aws.amazon.com/about-aws/whats-new/machine-learning
• Amazon Comprehend: 6 new languages
• Amazon Translate: 22 new languages
• Amazon Transcribe: 15 new languages, alternative transcriptions
• Amazon Lex: SOC compliance, sentiment analysis,
web & mobile integration with Amazon Connect
• Amazon Personalize: batch recommendations
• Amazon Forecast: use any quantile for your predictions
With region expansion across the board!

36. Introducing Amazon Transcribe Medical
Easy-to-UseAccurate Affordable

37. Introducing Amazon Rekognition Custom Labels
• Import images labeled by Amazon
SageMaker Ground Truth…
• Or label images automatically based on folder structure
• Train a model on fully managed
infrastructure
• Split the data set for training and validation
• See precision, recall, and F1 score at the end of training
• Select your model
• Use it with the usual Rekognition APIs

38. A2I lets you easily implement human review in
machine learning workflows to improve the accuracy,
speed, and scale of complex decisions.
Amazon Augmented AI (A2I)

39. How Amazon Augmented AI works
Client application
sends input data
AWS AI Service or
custom ML model
makes predictions
Results stored
to your S3
1 2
4
Low confidence predictions
sent for human review
3
High-confidence predictions
returned immediately to client
application
5
Amazon Rekognition
Amazon Textract

41. Introducing Amazon Fraud Detector
A fraud detection service that makes
it easy for businesses to use machine
learning to detect online fraud in
real-time, at scale

42. Amazon Fraud Detector – Automated Model Building
1 2 4 5
Training
data in S3
63

43. Introducing Contact Lens For Amazon Connect
Theme
detection
Built-in automatic
call transcription
Automated
contact
categorization
Enhanced
Contact Search
Real-time sentiment
dashboard
and alerting
Presents
recurring
issues based
on
Customer
feedback
Identify call types
such as script
compliance,
competitive
mentions,
and cancellations.
Filter calls of
interest based
on words
spoken and
customer
sentiment
View entire call
transcript directly in
Amazon Connect
Quickly identify
when customers
are having a
poor experience
on live calls
Easily use the power of machine learning to improve the quality of your customer experience
without requiring any technical expertise

45. Introducing AWS CodeGuru
Built-in code reviews
with intelligent
recommendations
Detect and optimize
expensive lines of
code before
production
Easily identify latency
and performance
improvements
production
environment
CodeGuru Reviewer CodeGuru Profiler

46. CodeGuru Reviewer: How It Works
Input:
Source Code
Feature Extraction Machine Learning
Output:
Recommendations
Customer provides source
code as input
Java
AWS CodeCommit
Github
Extract semantic features /
patterns
ML algorithms identify similar
code for comparison
Customers see
recommendations as Pull
Request feedback

47. CodeGuru Example – Looping vs Waiting
do {
DescribeTableResult describe = ddbClient.describeTable(new DescribeTableRequest().withTableName(tableName));
String status = describe.getTable().getTableStatus();
if (TableStatus.ACTIVE.toString().equals(status)) {
return describe.getTable();
}
if (TableStatus.DELETING.toString().equals(status)) {
throw new ResourceInUseException("Table is " + status + ", and waiting for it to become ACTIVE is not useful.");
}
Thread.sleep(10 * 1000);
elapsedMs = System.currentTimeMillis() - startTimeMs;
} while (elapsedMs / 1000.0 < waitTimeSeconds);
throw new ResourceInUseException("Table did not become ACTIVE after ");
This code appears to be waiting for a resource before it runs. You could use the waiters feature to help improve
efficiency. Consider using TableExists, TableNotExists. For more information,
see https://aws.amazon.com/blogs/developer/waiters-in-the-aws-sdk-for-java/
Recommendation
Code
We should use waiters instead - will help remove a lot of this code.Developer Feedback

48. CodeGuru Profiler – Example

49. LOWER COSTINCREASE IN CPU UTILIZATION
AMAZON PRIME DAY 2017 VS 2018

50. Introducing Kendra
Easy to find what you are
looking for
Fast search, and
quick to set up
Native connectors
(S3, Sharepoint,
file servers,
HTTP, etc.)
Natural language
Queries
NLU and
ML core
Simple API
and console
experiences
Code samples
Incremental
learning through
feedback
Domain
Expertise

51. Kendra connectors
…and more coming in 2020

53. ML Services

54. Pre:Invent highlights
https://aws.amazon.com/about-aws/whats-new/machine-learning
• Invoke Amazon SageMaker models in Amazon Quicksight
• Invoke Amazon SageMaker models in Amazon Aurora
• Deploy many models on the same Amazon SageMaker endpoint

55. Fully managed
infrastructure in SageMaker
Introducing Amazon SageMaker Operators for Kubernetes
Kubernetes customers can now train, tune, & deploy models in
Amazon SageMaker

56. Machine learning is iterative involving
dozens of tools and hundreds of
iterations
Multiple tools needed for
different phases of the
ML workflow
Lack of an integrated
experience
Large number of iterations
Cumbersome, lengthy processes, resulting in
loss of productivity
+
+
=

57. Introducing Amazon SageMaker Studio
The first fully integrated development environment (IDE) for machine learning
Organize, track, and
compare thousands of
experiments
Easy experiment
management
Share scalable notebooks
without tracking code
dependencies
Collaboration at
scale
Get accurate models for
with full visibility & control
without writing code
Automatic model
generation
Automatically debug errors,
monitor models, & maintain
high quality
Higher quality ML
models
Code, build, train, deploy, &
monitor in a unified visual
interface
Increased
productivity

59. Data science and collaboration
needs to be easy
Setup and manage resources
Collaboration across
multiple data scientists
Different data science
projects have different
resource needs
Managing notebooks and
collaborating across
multiple data scientists is
highly complicated
+
+
=

60. Introducing Amazon SageMaker Notebooks
Access your notebooks in
seconds with your corporate
credentials
Fast-start shareable notebooks
Administrators manage
access and permissions
Share your notebooks
as a URL with a single click
Dial up or down
compute resources
Start your notebooks
without spinning up
compute resources

62. Data Processing and
Model Evaluation involves a lot of
operational overhead
Building and scaling infrastructure
for data processing workloads is
complex
Use of multiple tools or services
implies learning and
implementing new APIs
All steps in the ML workflow need
enhanced security, authentication
and compliance
Need to build and manage tooling
to run large data processing and
model evaluation workloads
+
+
=

63. Introducing Amazon SageMaker Processing
Analytics jobs for data processing and model evaluation
Use SageMaker’s built-in
containers or bring your own
Bring your own script for
feature engineering
Custom processing
Achieve distributed
processing for clusters
Your resources are created,
configured, & terminated
automatically
Leverage SageMaker’s
security & compliance
features

64. Managing trials and experiments is
cumbersome
Hundreds of experiments
Hundreds of parameters
per experiment
Compare and contrast
Very cumbersome and
error prone
+
+
=

65. Introducing Amazon SageMaker Experiments
Experiment
tracking at scale
Visualization for
best results
Flexibility with
Python SDK & APIs
Iterate quickly
Track parameters & metrics
across experiments & users
Organize
experiments
Organize by teams, goals, &
hypotheses
Visualize & compare
between experiments
Log custom metrics &
track models using APIs
Iterate & develop high-
quality models
A system to organize, track, and evaluate training experiments

67. Debugging and profiling
deep learning is painful
Large neural networks
with many layers
Many connections
Additional tooling for analysis
and debug
Extraordinarily difficult
to inspect, debug, and profile
the ‘black box’
+
+
=

68. Automatic data
analysis
Relevant data
capture
Automatic error
detection
Improved productivity
with alerts
Visual analysis
and debug
Introducing Amazon SageMaker Debugger
Analyze and debug data
with no code changes
Data is automatically
captured for analysis
Errors are automatically
detected based on rules
Take corrective action based
on alerts
Visually analyze & debug
from SageMaker Studio
Analysis & debugging, explainability, and alert generation

70. Deploying a model is not the end, you
need to continuously monitor it in
production and iterate
Concept drift due to
divergence of data
Model performance can
change due to unknown
factors
Continuous monitoring of model
performance and data involves a
lot of effort and expense
Model monitoring is
cumbersome but critical
+
+
=

71. Introducing Amazon SageMaker Model Monitor
Automatic data
collection
Continuous
Monitoring
CloudWatch
Integration
Data is automatically
collected from your
endpoints
Automate corrective
actions based on Amazon
CloudWatch alerts
Continuous monitoring of models in production
Visual
Data analysis
Define a monitoring
schedule and detect
changes in quality against
a pre-defined baseline
See monitoring results,
data statistics, and
violation reports in
SageMaker Studio
Flexibility
with rules
Use built-in rules to
detect data drift or write
your own rules for
custom analysis

73. Successful ML requires
complex, hard to discover
combinations
Largely explorative &
iterative
Requires broad and
complete
knowledge of ML domain
Lack of visibility
Time consuming,
error prone process
even for ML experts
+
+
=
of algorithms, data, parameters

74. Introducing Amazon SageMaker Autopilot
Quick to start
Provide your data in a
tabular form & specify target
prediction
Automatic
model creation
Get ML models with feature
engineering & automatic model
tuning automatically done
Visibility & control
Get notebooks for your
modelswith source code
Automatic model creation with full visibility & control
Recommendations &
Optimization
Get a leaderboard & continue
to improve your model

76. Ground
Truth
Algorithms
& Frameworks
Collaborative
notebooks
ExperimentsDistributed
Training &
Debugger
Deployment,
Monitoring, & Hosting
SageMaker AutoPilot
Build, Train, Deploy Machine Learning Models Quickly at Scale
Reinforcement
Learning
Tuning
& Optimization
SageMaker Studio
Marketplace
for ML
Amazon SageMaker

77. AWS DeepRacer improvements
• AWS DeepRacer Evo
• Stereo camera
• LIDAR sensor
• New racing opportunities
• Create your own races
• Object Detection & Avoidance
• Head-to-head racing

78. AWS DeepComposer
• MIDI keyboard to experiment with
music generation using ML
• Compose music using Generative
Adversarial Networks (GAN)
• Use a pretrained model, or train
your own

79. Frameworks and Infrastructure

80. Deep Graph Library
https://www.dgl.ai
• Python open source library that helps
researchers and scientists quickly build,
train, and evaluate Graph Neural Networks
on their data sets
• Use cases: recommendation, social
networks, life sciences, cybersecurity, etc.
• Available in Deep Learning Containers
• PyTorch and Apache MXNet, TensorFlow coming soon
• Available for training on Amazon
SageMaker

81. Deep Java Library
https://www.djl.ai
• Java open source library,
to train and deploy models
• Framework agnostic
• Apache MXNet for now, more will come
• Train your own model, or use a
pretrained one from the model
zoo

82. Databases & Analytics

83. Amazon Managed Apache Cassandra Service
Introducing
A scalable, highly available, and serverless Apache Cassandra–compatible
database service. Run your Cassandra workloads in the AWS cloud using the
same Cassandra application code and developer tools that you use today.
Apache Cassandra-
compatible
Performance
at scale
Highly available
and secure
No servers
to manage
DRAFTDatabases
Preview – December 3
LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service

84. Amazon RDS Proxy
Introducing
Fully managed, highly available database proxy feature for Amazon
RDS. Pools and shares connections to make applications more
scalable, more resilient to database failures, and more secure.
DRAFTDatabases
Public Beta – December 3
LEARN MORE DAT368: Setting up database proxy servers with RDS Proxy

85. UltraWarm for Amazon Elasticsearch Service
Introducing
A low cost, scalable warm storage tier for Amazon Elasticsearch Service. Store
up to 10 PB of data in a single cluster at 1/10th the cost of existing storage tiers,
while still providing an interactive experience for analyzing logs.
DRAFTAnalytics
Public Beta – December 3
LEARN MORE ANT229: Scalable, secure, and cost-effective log analytics

86. DRAFTAnalytics
Amazon Redshift RA3 instances with Managed Storage
Optimize your data warehouse costs by paying for compute and storage separately
General Availability – December 3
L E A R N M O R E
ANT213-R1: State of the Art Cloud Data Warehousing
ANT230: Amazon Redshift Reimagined: RA3 and AQUA
Delivers 3x the performance of existing cloud DWs
2x performance and 2x storage as similarly priced
DS2 instances (on-demand)
Automatically scales your DW storage capacity
Supports workloads up to 8PB (compressed)
COMPUTE NODE
(RA3/i3en)
SSD Cache
S3 STORAGE
COMPUTE NODE
(RA3/i3en)
SSD Cache
COMPUTE NODE
(RA3/i3en)
SSD Cache
COMPUTE NODE
(RA3/i3en)
SSD Cache
Managed storage
$/node/hour
$/TB/month
Introducing

87. AQUA (Advanced Query Accelerator) for Amazon Redshift
Introducing
Redshift runs 10x faster than any other cloud data warehouse without increasing cost
DRAFTAnalytics
Private Beta – December 3
LEARN MORE ANT230: Amazon Redshift Reimagined: RA3 and AQUA
AQUA brings compute to storage so data doesn't have
to move back and forth
High-speed cache on top of S3 scales out to process
data in parallel across many nodes
AWS designed processors accelerate data compression,
encryption, and data processing
100% compatible with the current version of Redshift
S3
STORAGE
AQUA
ADVANCED QUERY ACCELERATOR
RA3 COMPUTE CLUSTER

88. Amazon Redshift Federated Query
Analyze data across data warehouse, data lakes, and operational
database
New Feature
DRAFTAnalytics
Public Beta – December 3
LEARN MORE ANT213-R1: State of the Art Cloud Data Warehousing

89. Amazon Redshift Data Lake Export
New Feature
No other data warehouse makes it as easy to gain new insights from
all your data.
DRAFTAnalytics
General Availability – December 3
LEARN MORE ANT335R: How to build your data analytics stack at scale with Amazon Redshift

90. DRAFTDatabases
Announced – November 26
Amazon Aurora Machine Learning Integration
Simple, optimized, and secure Aurora, SageMaker, and Comprehend (in preview)
integration. Add ML-based predictions to databases and applications using SQL,
without custom integrations, moving data around, or ML experience.
New Feature

91. Amazon Athena Federated Query
Analyze data across any data source via Data Source Connectors
that run on AWS Lambda (SAR)
New Feature
DRAFTAnalytics
Public preview – November 26

92. AWS Data Exchange
Quickly find diverse data
in one place
Efficiently access
3rd-party data
Easily analyze data
Reach millions of
AWS customers
Easiest way to package and
publish data products
Built-in security and
compliance controls
For
Subscribers
For
Providers
DRAFTAnalytics
Announced – November 13
L E A R N M O R E ANT238-R: AWS Data Exchange: Easily find & subscribe to third-party data in the cloud
Easily find and subscribe to 3rd-party data in the cloud

93. Networking

94. Existing Service
DRAFTNetworking
Scale connectivity across thousands
of Amazon VPCs, AWS accounts,
and on-premises networks
Amazon VPCAmazon VPC
Amazon VPCAmazon VPC
Customer
gateway
VPN
connection
AWS Direct
Connect Gateway
L E A R N M O R E NET203-L Leadership Session Networking
AWS Transit Gateway

95. New Feature
AWS Transit Gateway Inter-Region Peering
General Availability – December 3
DRAFTNetworking
AWS TRANSIT
GATEWAY
Inter-Region Peering
Build global networks by connecting transit gateways across multiple AWS Regions
L E A R N M O R E NET203-L Leadership Session Networking

96. High availability and improved performance of site-to-site VPN
New Feature
AWS Accelerated Site-to-Site VPN
General Availability – December 3
DRAFTNetworking
L E A R N M O R E NET203-L Leadership Session Networking

97. AWS Transit Gateway Network Manager
Introducing General Availability – December 3
DRAFTNetworking
L E A R N M O R E NET212 - AWS Transit Gateway Network Manager

98. New Feature
Transit Gateway Multicast
General Availability – December 3
DRAFTNetworking
Build and deploy multicast applications in the cloud
L E A R N M O R E NET203-L Leadership Session Networking

99. New Feature
Amazon VPC Ingress Routing
General Availability – December 3
DRAFTNetworking
Route inbound and outbound traffic through a third party or AWS service
L E A R N M O R E NET203-L Leadership Session Networking

100. Security

101. DRAFTManagement Tools
Announced – November 21
Identify unusual (write) activity in your AWS accounts
ü Save time sifting through logs
ü Get ahead of issues before
they impact your business
AWS CloudTrail Insights
Introducing
• Unexpected spikes in resource
provisioning
• Bursts of IAM management
actions
• Gaps in periodic maintenance
activity

102. Amazon Detective
Introducing
Quickly analyze, investigate, and identify the root cause of security
findings and suspicious activities.
Automatically distills
& organizes data into
a graph model
Easy to use visualizations
for faster & effective
investigation
Continuously updated as
new telemetry becomes
available
Preview – December 3
DRAFTSecurity
LEARN MORE SEC312: Introduction to Amazon Detective

103. AWS IAM Access Analyzer
Introducing
Continuously ensure that policies provide the intended public and cross-account access
to resources, such as Amazon S3 buckets, AWS KMS keys, & AWS Identity and Access
Management roles.
General Availability – December 2
DRAFTSecurity
Uses automated reasoning, a form of
mathematical logic, to determine all possible
access paths allowed by a resource policy
Analyzes new or updated resource
policies to help you understand
potential security implications
Analyzes resource policies for
public or cross-account access
LEARN MORE SEC309: Deep Dive into AWS IAM Access Analyzer

104. 1
Create or use existing identities, including Azure AD, and manage access
centrally to multiple AWS accounts and business applications, for easy
browser, command line, or mobile single sign-on access by employees.
New Feature
AWS Single Sign-On - Azure AD Support
Announced – November 25
DRAFTSecurity

105. Extending AWS beyond
the Region

106. What customers are doing with AWS IoT
Remotely monitor
patient health &
wellness applications
Manage energy resources
more efficiently
Enhance safety in
the home, the office,
and the factory floor
Transform transportation with
connected and autonomous
vehicles
Track inventory
levels and manage
warehouse operations
Improve the performance
and productivity of industrial
processes
Build smarter products & user
experiences in homes,
buildings, and cities
Grow healthier crops with
greater efficiencies

107. Alexa Voice Service (AVS) Integration for IoT Core
New Feature
DRAFTInternet of Things
Announced – November 25
Quickly and cost effectively go to market with Alexa built-in capabilities on new categories of products
such as light switches, thermostats, and small appliances.
Accelerate time to market with
certified partner development kits
that work with AVS Integration for IoT
Core by default.
Lowers the cost of integrating Alexa Voice
up to 50% by reducing the compute and
memory footprint required
Build new categories of Alexa Built-in
products on resource constrained devices
(e.g. ARM ‘M' class microcontrollers with
<1MB embedded RAM).

108. Container Support for AWS IoT Greengrass
New Feature
DRAFTInternet of Things
Announced – November 25
Deploy containers seamlessly to edge devices
Move containers from the cloud
to edge devices using AWS IoT
Greengrass, without rewriting
any code.
Enables both Docker & AWS
Lambda components to
operate seamlessly together at
the edge
Use AWS IoT Greengrass Secrets
Manager to manage credentials
for private container registries.

109. AWS Outposts
Now Available
Fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any
connected customer site. Truly consistent hybrid experience for applications across on-premises and
cloud environments. Ideal for low latency or local data processing application needs.
Same AWS-designed infrastructure
as in AWS regional data centers
(built on AWS Nitro System)
delivered to customer facilities
Fully managed, monitored, and
operated by AWS
as in AWS Regions
Single pane of management
in the cloud providing the
same APIs and tools as
in AWS Regions
Compute
General Availability – December 3
LEARN MORE CMP302-R: AWS Outposts: Extend the AWS experience to on-premises environments

111. Services supported on Outposts
(additionally to EC2 & EBS)

112. Local Zones
Introducing
Extend the AWS Cloud to more locations and closer to your end-users
to support ultra low latency application use cases. Use familiar AWS
services and tools and pay only for the resources you use.
DRAFTCompute
General Availability – December 3
The first Local Zone to be released will be located in Los Angeles.

113. AWS Wavelength
Introducing
Embeds AWS compute and storage inside telco providers’ 5G
networks. Enables mobile app developers to deliver applications with
single-digit millisecond latencies. Pay only for the resources you use.
DRAFTCompute
Announcement – December 3

114. AWS Wavelength
Introducing
Embeds AWS compute and storage inside telco providers’ 5G
networks. Enables mobile app developers to deliver applications with
single-digit millisecond latencies. Pay only for the resources you use.
DRAFTCompute
Announcement – December 3

115. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

116. Here are all of the new launches!
https://aws.amazon.com/new/reinvent

119. Go Build!
Here to help you build.

120. Thank you.
Chris Fregly
Developer Advocate, AI & Machine Learning
Amazon Web Services
@cfregly
https://aws.amazon.com/new/reinvent