Re:Cap of 2019 re:Invent covering some of the main releases.

1. Pret oria
Cobus Bernard
Senior Developer Advocate
Amazon Web Services

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

3. Amazon Confidential
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 Wednesday, 7pm, Aria
Natural language
processing
PersonalizationObject
detection
Speech
recognition
Image processing Fraud
detection

4. Amazon Confidential
Introducing Amazon EC2 Inferentia
• Fast, low-latency inferencing at a very low cost
• 64 teraOPS on 16-bit floating point (FP16 and BF16) and mixed-precision data.
• 128 teraOPS on 8-bit integer (INT8) data.
• Neuron SDK: https://github.com/aws/aws-neuron-sdk
• Available in Deep Learning AMIs and Deep Learning Containers
• TensorFlow and Apache MXNet, PyTorch coming soon
Instance Name Inferentia Chips vCPUs RAM EBS Bandwidth
inf1.xlarge 1 4 8 GiB Up to 3.5 Gbps
inf1.2xlarge 1 8 16 GiB Up to 3.5 Gbps
inf1.6xlarge 4 24 48 GiB 3.5 Gbps
inf1.24xlarge 16 96 192 GiB 14 Gbps

5. Amazon Confidential
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 Wednesday 9:15am, MGM

6. Amazon Confidential
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 Wednesday 9:15am, MGM

7. Amazon Confidential
SPEC cpu2017
• Industry standard CPU
intensive benchmark
• Run on all vCPUs concurrently
• Comparing performance/vCPU
* All SPEC scores estimates, compiled with GCC9 -O3 -march=native,
run on largest single socket size for each instance type tested.
40%
60%
80%
100%
120%
140%
160%
SPECint2017 Rate SPECfp2017 rate
Performance/vCPU
SPECcpu2017 Rate*
M5 M6G
DRAFTCompute

8. Amazon Confidential
SPEC jvm2008
• Java VM benchmark
• Run across all vCPUs concurrently
• Comparing performance/vCPU
* All SPEC scores estimates, run with OpenJDK11 and skipping compiler* and startup.* tests
Tests run on largest single-socket instance size for each instance type tested.
40%
60%
80%
100%
120%
140%
160%
Performance/vCPU
SpecJVM*
M5 M6G
DRAFTCompute

9. Amazon Confidential
Load Balancing with Nginx
40%
60%
80%
100%
120%
140%
160%
Performance(Requests/s)
M5 M6G
Load
Balancer
(nginx)
NGINX v1.15.9, 512 clients, 128 GET/POST payloads, all HTTPS connections, AES128-GCM-SHA256,
OpenSSL 1.1.1, 4 target machines, all tests run on 4xl size; load generator c5.9xl; web servers c5.4xls;
All servers run in a cluster placement group
DRAFTCompute

10. Amazon Confidential
Memcached
40%
60%
80%
100%
120%
140%
160%
Performance(Requests/s)
Throughput
M5 M6G
Memcached
Memcached v1.5.16, 16B keys, 128B values, 7.8M KV-pairs, 576 connections for load generation from 2x c5.9xlarge
instances, 16 additional connections used to measure latency from 1 additional c5.9xlarge,;each connection maintains
4096 outstanding requests; All servers in a cluster placement group
DRAFTCompute

11. Amazon Confidential
Media Encoding with x264
• Huge amount of video created daily
• Encoding it reduces bandwidth to
deliver and storage of that video
• Using libx264 encoder encoded
uncompressed 1080p to h264 40%
60%
80%
100%
120%
140%
160%
Performance(Frames/s)
M5 M6G
DRAFTCompute

12. Amazon Confidential
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 Wednesday 11:30am, Venetian

13. Amazon Confidential
AWS Compute Optimizer
Introducing
Identify optimal Amazon EC2 instances and EC2 Auto Scaling group
for your workloads using a ML-powered recommendation engine
DRAFTManagement Tools
General Availability – December 3
LEARN MORE CMP323: Optimize Performance and Cost for Your AWS Compute Wednesday, 10:45am, MGM

14. Amazon Confidential
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 Wednesday, 5:30pm
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

15. Amazon Confidential
DRAFTContainers
General Availability – December 3
LEARN MORE CON-326R - Running Kubernetes Applications on AWS Fargate
Wednesday, 4pm, Aria
Thursday, 1:45pm, MGM
Introducing
The only way to run serverless Kubernetes containers securely,
reliably, and at scale
Amazon EKS for AWS Fargate

16. Amazon Confidential
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

17. Amazon Confidential
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

18. Amazon Confidential
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

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

20. Amazon Confidential
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
LEARN MORE
WIN201 - Leadership session: Five New Features of Microsoft and
.NET on AWS that you want to learn
Tuesday, 4pm, MGM

21. Amazon Confidential
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

22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

23. Amazon Confidential
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.
DRAFTStorage
General Availability – December 3

24. Amazon Confidential
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 faster backups for EBS volumes at lower costs.
L E A R N M O R E CMP305-R: Amazon EBS snapshots: What’s new, best practices, and security Thursday,1:00pm, MGM
Up to 70% faster
backup times
More granular recovery
point objectives (RPOs)
Lower cost backups
Amazon Confidential
Compute
Easily track incremental
block changes on EBS
volumes to achieve:
General Availability – December 3

25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

26. Amazon Confidential
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

27. Amazon Confidential
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.

28. Amazon Confidential
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

29. Amazon Confidential
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

30. Amazon Confidential
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
Wednesday, 10am, Venetian
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
S 3 S T O R A G E
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

31. Amazon Confidential
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 Wednesday, 10am, Venetian
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
S 3
S T O R A G E
AQUA
ADVANCED QUERY ACCELERATOR
R A 3 C O M P U T E C L U S T E R

32. Amazon Confidential
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 Tuesday, 3pm, Bellagio

33. Amazon Confidential
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
Monday, 7pm, Venetian
Tuesday, 11:30am, Aria

34. Amazon Confidential
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
Thursday, 2:30pm, Venetian
Easily find and subscribe to 3rd-party data in the cloud

35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

36. Amazon Confidential
DRAFTManagement Tools
Announced – November 21
Identify unusual activity in your AWS accounts
 Save time sifting through logs
 Get ahead of issues before
they impact your business
CloudTrail Insights
Introducing
• Unexpected spikes in resource
provisioning
• Bursts of IAM management
actions
• Gaps in periodic maintenance
activity
L E A R N M O R E MGT420-R: CloudTrail Insights: Identify and Solve Operational Issues

37. Amazon Confidential
AWS 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 Thursday, 1:45pm, Venetian

38. Amazon Confidential
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 Thursday, 3:15pm, Venetian

39. Amazon Confidential
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
Announced – November 25
DRAFTSecurity
LEARN MORE SEC308: Manage federated user permissions at scale with AWS SSO Thursday, 12:15pm, Venetian

40. Amazon Confidential
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 Wednesday, 11:30am, MGM
AWS Transit Gateway

41. Amazon Confidential
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 Wednesday, 11:30am, MGM

42. Amazon Confidential
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

43. Amazon Confidential

44. Amazon Confidential
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 Wednesday, 11:30am, MGM

45. Amazon Confidential
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 Wednesday, 11:30am, MGM

46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

47. Amazon Confidential
L E A R N M O R E SVS401 - Optimizing your serverless applications
Wednesday, 1:45pm, Mirage
Thursday, 3:15pm, Venetian
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

48. Amazon Confidential
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)
Wednesday 9:15am, Venetian

49. Amazon Confidential
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 Wednesday, 3:15pm, MGM

50. Amazon Confidential
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)
Wednesday 9:15am,
Venetian

51. Amazon Confidential
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 Wednesday, 11:30am, Venetian

52. Amazon Confidential
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 Wednesday, 2:30pm, Mirage

53. Amazon Confidential
57

54. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

55. Amazon Confidential
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

56. Amazon Confidential
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).

57. Amazon Confidential
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.

58. Amazon Confidential
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
Wednesday at 11:30am, Aria
Thursday at 3:15pm, Mirage
Friday at 10:45am, Mirage

59. Amazon Confidential
Additional AWS Services Supported Locally on Outposts

60. Amazon Confidential
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.

61. Amazon Confidential
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

62. Amazon Confidential
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

63. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AI & Machine Learning Launches
Julien Simon
Global Evangelist, AI & Machine Learning
Amazon Web Services
@julsimon

64. Please fasten your seatbelts!

65. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

66. 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!

67. Speech Recognition
For Healthcare

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

69. Custom Image Models

70. 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

71. Human In the Loop

72. Customers are forced to choose
ML only systems are high speed and low
cost, but do not support nuanced decision
making
Human only workflows offer nuanced
decision making, but they’re low speed and
high cost.
OR

73. Customers need
+
Machine Learning and humans working together

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

75. 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

76. Human Review Workforces
Amazon Mechanical Turk
An on-demand 24x7 workforce
of over 500,000 independent
contractors worldwide, powered
by Amazon Mechanical Turk
Private
A team of workers that you have
sourced yourself, including your
own employees or contractors
for handling data that needs to
stay within your organization
Vendors
A curated list of third-party
vendors that specialize in
providing data labeling services,
available via de AWS Marketplace

77. Fraud Detection

78. Fraud detection is difficult
$$$ billions lost to
fraud each year
Online business prone
to fraud attacks
Bad actors often
change tactics
Changing rules =
more human reviews
Dependent on others to
update detection logic

79. Fraud detection with ML is also difficult
Top data scientists are
costly & hard to find
One-size-fits-all models
underperform
Often need to
supplement data
Data transformation +
feature engineering
Fraud imbalance =
needle in a haystack

80. 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

81. Amazon Fraud Detector – Key Features
Pre-built fraud
detection model
templates
Automatic
creation of
custom fraud
detection
models
Models learn
from past
attempts to
defraud Amazon
Amazon
SageMaker
integration
One interface to
review past
evaluations and
detection logic

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

83. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

84. Speech & Text Analytics
For Contact Centers

85. Challenges in contact centers
• Better visibility into quality of customer interactions
• Cost prohibitive
•
• Timely discovery of emerging issues
• Support for live calls
• End user experience

86. 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

89. Improving code quality

90. Typical Application Build and Run Process
Write +
Review
Build +
Test
Deploy Measure Improve
1. Code Reviews require expertise in multiple areas such as
knowledge of AWS APIs, Concurrency, etc.
2. Code analyzer tools require high accuracy.
3. Distributed Cloud application are difficult to optimize.
4. Performance engineering expertise is hard to find.

91. 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

92. 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

93. 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

94. CodeGuru Profiler: How It Works
Input:
Live application stack
trace
Application profile
sampling
Pattern matching
Output:
Method names,
Recommendations
and searchable
visualizations
Customer application
runs in production
CodeGuru Profiler
continuously captures
application stack trace
information
CodeGuru Profiler detects
performance inefficiencies in
the live application
Customers see
recommendations in their
automated efficiency reports
and visualizations
Amazon Confidential

95. CodeGuru Profiler – Example

96. CodeGuru Profiler Visualization – Hotspots
Java.util.zip.Deflator.deflateBytes 61%
LCMS.colorEvent 6%JPEGImageReader.readImage 5%

97. Enterprise Search

98. Employees spend 20% of their
time looking for information.
—McKinsey
20%
44%44% of the time, they cannot
find the information they need to
do their job.
—IDC

99. 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

100. Kendra connectors
…and more coming in 2020

101. Getting started with Kendra
Step 1
Create an index
An index is the place where
you add your data sources
to make them searchable
in Kendra.
Step 2
Add data sources
Add and sync your data
from S3, Sharepoint, Box
and other data sources, to
your index.
Step 3
Test & deploy
After syncing your data,
visit the Search console
page to test search &
deploy Kendra in your
search application.

103. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

104. 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

105. Using Kubernetes for ML is hard to
manage and scale
Build and manage services
within Kubernetes cluster for ML
Make disparate open-source
libraries and frameworks work
together in a secure and
scalable way
Requires time and expertise from
infrastructure, data science, and
development teams
Need an easier way to use
Kubernetes for ML
+
+
=

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

107. 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
+
+
=

108. 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

110. 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
+
+
=

111. 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

112. 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
+
+
=

113. 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

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

115. 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

116. 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’
+
+
=

117. 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

118. 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
+
+
=

119. 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

120. 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

121. 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

122. 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

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

124. AWS DeepComposer
• The world’s first machine
learning-enabled musical
keyboard
• Compose music using Generative
Adversarial Networks (GAN)
• Use a pretrained model, or train
your own

125. Enjoy!
Build cool stuff,
and please send us feedback!

126. Amazon Confidential
Launch Announcements covered in this session
Amazon EC2 Inf1 Instances
AWS Graviton2 Processor
AWS Graviton2 Based Instances
Amazon Braket
AWS Nitro Enclaves
AWS Compute Optimizer
Savings Plans
Amazon EKS for AWS Fargate
AWS Fargate Spot
Amazon ECS Capacity Providers
Amazon ECS CLI 2.0
ECS Cluster Autoscaling
EC2 Image Builder
AWS License Manager - Simplified
Windows &SQL Server BYOL
AWS End of support Migration Program
for Windows Server
Amazon S3 AccessPoints
EBS Direct APIs for Snapshots
Amazon Managed Apache Cassandra
Service
Amazon Aurora machine learning
integration
Amazon RDS Proxy
UltraWarm for Amazon Elasticsearch
Service
Amazon Redshift RA3 instances with
Managed Storage
AQUA (Advanced Query Accelerator) for
Amazon Redshift
Amazon Redshift Federated Query
Amazon Redshift Data LakeExport
AWS Data Exchange
CloudTrail Insights
AWS Detective
AWS IAM AccessAnalyzer
AWS Single Sign-On
AWS Transit Gateway Inter-Region
Peering
AWS Accelerated Site-to-Site VPN
AWS Transit Gateway Network Manager
Transit Gateway Multicast
Amazon VPC Ingress Routing
Provisioned Concurrency on AWS
Lambda
HTTP APIs for Amazon API Gateway
AWS Step Functions Express Workflows
Amazon EventBridge Schema Registry
Amplify for iOS & Android
Amplify DataStore
The Amazon Builders’ Library
Alexa Voice Service (AVS) Integration for
IoT Core
Container Support for AWS IoT
Greengrass
AWS Outposts
LocalZones
Wavelength Zones

127. Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cobus Bernard
Senior Developer Advocate
Amazon Web Services
@cobusbernard