ReInvent 2019 reCap Nordics

N o rd i cs
Marcia Villalba
Developer Advocate, AWS
@mavi888uy

Agenda
Topics for the day:
• Compute
• Storage
• Database and analytics
• Networking
• Serverless
• Infrastructure
• AI services
• ML services

Please fasten your seatbelts!

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

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

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

DRAFTContainers
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

The Amazon Builders’ Library
Architecture, software delivery, and operations
By Amazon’s senior technical executives and engineers
Real-world practices with detailed explanations
Content available for free on the website

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

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
LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service

DRAFTDatabases
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.

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

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

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

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
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

DRAFTManagement Tools
Identify unusual activity in your AWS accounts
ü Save time sifting through logs
ü 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

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
DRAFTSecurity
LEARN MORE SEC312: Introduction to Amazon Detective

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.
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

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

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
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)

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
L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions

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
LEARN MORE
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)

Lambda Destinations
Introducing

Container Support for AWS IoT Greengrass
New Feature
DRAFTInternet of Things
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.

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
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

Amazon EC2
Amazon EBS
Amazon ECS
Amazon EKS
Amazon EMR
Amazon VPC
Amazon RDS
Amazon S3

Additional AWS Services Supported Locally on Outposts

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
The first Local Zone to be released will be located in Los Angeles.

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

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
(Inf2)
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

Introducing Amazon Transcribe Medical
Easy-to-UseAccurate Affordable

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

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

Customers need
+
Machine Learning and humans working together

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)

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

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

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

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

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

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

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.

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
LEARN MORE Introduction to Amazon CodeGuru (DOP211)

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

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

LOWER COSTINCREASE IN CPU UTILIZATION
AMAZON PRIME DAY 2017 VS 2018

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

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

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

Kendra connectors
…and more coming in 2020

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.

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

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
+
+
=

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

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
+
+
=

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

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

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

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’
+
+
=

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

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
+
+
=

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

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

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

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

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

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

T h a n k y o u !
Marcia Villalba
Developer Advocate, AWS
@mavi888uy

ReInvent 2019 reCap Nordics

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ReInvent 2019 reCap Nordics