Best of re:Invent for Startups

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Best of re:Invent
Itzik Paz
Startup Solution Architect

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Machine Learning and Artificial Intelligence
Ground Truth, Textract, Forecast, Personalize SageMaker RL and Neo
Databases
The right data store for the job: Timestream, DynamoDB on-demand & transactions, QLDB
Blockchain
QLDB again, Managed Blockchain
Serverless
Lambda Runtime Layers, Lambda ALB targets, API Gateway WebSockets support

Put machine learning in the
hands of every developer
and data scientist
ML @ AWS
OUR MISSION

Vision Speech Language Chatbots
LEXPOLLYREKOGNITION
IMAGE
REKOGNITION
VIDEO
TRANSCRIBE TRANSLATE COMPREHEND
A M A Z O N S A G E M A K E R
B U I L D T R A I N
Forecasting Recommendations
Pre-built algorithms & notebooks
Ground Truth (data labeling)
One-click model training & tuning
Optimization (N E O )
Frameworks Interfaces Infrastructure
EC2 P3 EC2 C5 FPGAs GREENGRASS
FORECAST PERSONALIZE
ELASTIC
INFERENCE
Reinforcement learning
AWS Marketplace for Machine Learning
D E P L O Y
One-click deployment &
hosting
A I
S E R V I C E S
M L
S E R V I C E S
M L F R A M E W O R K S
&
I N F R A S T R U C T U R E
TEXTRACT
& P3dn

Fully managed
hosting with
auto-scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
BUILD TRAI N & TUNE DEPLOY
Build, train, tune, and host your own models
Hyperparameter
optimization
End-to-end-encryption
End-to-end VPC support
Compliance and audit capabilities
Metadata and experiment management capabilities
Pay as you go
Amazon SageMaker So, what’s new?

Successful models require high-quality data

Amazon SageMaker Ground Truth
Label machine learning training data easily and accurately

Amazon SageMaker Ground Truth - How It Works

Types of Machine Learning
Supervised learning
Unsupervised learning
AMOUNT OF TRAINING DATA REQUIRED
SOPHISTICATIONOFMLMODELS
Reinforcement
Learning
(RL)

Amazon SageMaker RL
Reinforcement learning for every developer and data scientist
Broad support
for frameworks
Broad support for simulation
environments including
SimuLink and MatLab
Fully
managed

Amazon SageMaker Neo
Train once, run anywhere with 2x the performance

Amazon SageMaker Neo - How It Works

Register with AWS Marketplace
Automatic validation on SageMaker
Package algorithm, models and configuration
Self-service listing on AWS Marketplace
Browse or search
AWS Marketplace
Subscribe
in a single click
Available through
Amazon SageMaker
Over a hundred algorithms and models that can
be deployed directly to Amazon SageMaker
AWS Marketplace for Machine Learning
S e l l i n g a l g o r i t h m s &
m o d e l s o n A W S
M a r k e t p l a c e

Not a data scientist, but want to use ML?

Popularity Trap
Only showing most popular
items limits the individuality of
the recommendation
Cold Starts
Relevant recommendations must be able to be
surfaced even for customers with limited history
Scale
Resonant recommendations need to scale
across thousands of products and customers
Real-Time
Personalization must work at low latency, and be
responsive to the changing intent of a customer

Real-time Works with almost any
product or content
Deliver high quality
recommendations
Easy to Use

Activity stream
Views, signups, conversion, etc.
Inventory
Videos, products, articles, etc.
Customized
Personalization
APIDemographics (optional)
Name, age, location, etc.
1. Load data
2. Inspect data
3. Identify features
4. Select algorithms
5. Select hyperparameters
6. Train models
7. Optimize models
8. Build feature store
9. Deploy and host models
10.Create real-time caches
Amazon Personalize

The perils of
poor predictions
in forecasting
TIME
SALES
FORECAST
SALES

Easy to use, through
the console and API
Works with any
historical time-series
More accurate
forecasts that integrate
external data

the 137 protein structures
The performance of the various clusterings was evalu-
MethOd Num' C'USte'S Rand mdex ated using two types of measures.
The first is the average
TM~score 8 89.7% silhouette width itself, which is a measure of the
clus-
ppm 9 39,396 ter compactness and separation. In general, clustering is
305C 9 895% based on the assumption that the underlying data form
compact clusters of similar characteristics. Larger aver-
R50 7 92.096
age Silhouette Width means that the result of a clustering
Traditional OCR only provides a “bag of letters”

Amazon Textract
Extract text and data from virtually any document
Eliminate
manual effort
Lower document
processing costs
Extract data quickly and
accurately

Databases: There is no one size fits all
Purpose-built databases and analytic engines.
Right tool for the right job.

pre:Invent AWS databases services
DynamoDB NeptuneRDS ElastiCache
Relational Key-value Document In-memory Graph

Existing time-series databasesRelational databases
Difficult to
maintain high
availability
Difficult to
scale
Limited data
lifecycle
management
Inefficient
time-series data
processing
Unnatural for
time-series
data
Rigid schema
inflexible for fast
moving time-
series data
Building with time-series data is challenging

Amazon Timestream
Fast, scalable, and fully managed time series database
1,000x faster at 1/10th the
cost of relational databases
Trillions of daily
events
Analytics optimized
for time series data
Serverless

Amazon Timestream – How It Works

Database capacity planning is hard

DynamoDB on-demand
Key Benefits
• No capacity planning, provisioning, or
reservations– simply make API calls
• Pay only for the reads and writes you perform
• Eliminates tradeoffs of over- or under-
provisioning
• Instantly accommodates your workload as
traffic ramps up or down

DynamoDB Transactional APIs
Balance financial
transactions
Process orders Coordinate
actions in multi-
player games
Update metadata
in distributed
systems

AWS databases services
DynamoDB NeptuneRDS TimestreamElastiCache
Relational Key-value Document In-memory Graph Time series Ledger

Blockchain

Centralized ledgers use cases
Healthcare
Verify and track providers
and insurance
DMV
Track vehicle title
history
Manufacturers
Track distribution of a
recalled product
HR & Payroll
Track changes to an
individual’s profile

Challenges with building centralized ledgers
Adds unnecessary
complexity
BlockchainRDBMS - audit tables
SlowHard to use
and maintain
Hard to build
Custom audit functionality using
triggers or stored procedures
Impossible to verify
No way to verify changes
made to data by sys admins

Amazon QLDB - How It Works
Centralized ledgers

Uses cases for distributed ledgers
Small Businesses
Transact with suppliers
and distributors
Financial Institutions
Peer-to-peer payments
Mortgage Lenders
Processing syndicated
loans
Retail
Streamline customer
rewards

Challenges with building blockchain networks
Set up is hard Hard to scale Complicated to
manage
Expensive

Add network
members
Add nodes
Deploy
applications
Create a
network
Amazon Managed Blockchain - How It Works
Blockchain networks

Serverless

Lambda Layers Runtime API
Features which allow developers to share, discover, and deploy
both libraries and languages as part of their serverless applications
Runtime API enables developers
to use Lambda with any
programming language
Layers let functions easily
share code. Upload layer once,
reference within any function
Layers promote separation of
responsibilities, lets developers
focus on writing business logic
Combined, Runtime API and
Layers allow developers to share
any programming language or
language version with others

Lambda as a Target for Application Load Balancer
Allows for serverless application architectures to be
registered as a load balancing target
Invoke Lambda functions to serve HTTP(S)
requests
Greater flexibility to mix-and-match servers
and serverless compute for applications
using a single HTTP endpoint
Robust load balancer controls

Amazon API Gateway supports WebSocket APIs
Enabling customers to build real-time two-way communication capabilities
within applications without managing connected users and devices
Fully managed APIs
to handle connections and messages
transfer between users and backend
services
No servers to manage
provision, or scale and
less code complexity
Powers engaging customer experiences
within applications such as chat, alerts
and notifications, and real-time dashboards

Thank you!

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