Speaker: Ben Snively, Principal Solutions Architect - Data & Analytics, AWS Note: This is part 2 of the deck. Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session learn about the new services announced at re: Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.

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Accelerating ML adoption with our
new AI services
Ben Snively
Specialist Solutions Architect
AWS

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Real-time Works with almost any
product or content
K E Y F E AT U R E S
Responsive to changes
in intent
Automated
machine learning
Bring existing algorithms
from Amazon SageMaker
Deliver high quality
recommendations
Deep learning
enabled algorithms
Easy to Use

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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
Amazon Personalize – How it works

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Amazon Personalize – Algorithms
Use AutoML or pick a
pre-defined algorithm
• Picking an algorithm:
• Choose a pre-existing
algorithm (packaged as
Personalize Recipes) or
• AutoML and Personalize will
pick the right recipe for
• factorization machine
using a deep autoencoder
• hierarchical recurrent
neural network

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Amazon Personalize: Real-time data and Batch data ingestion
Historical user
activity
User
attributes
Item
catalog
Real-time data
Mobile
SDKs
(coming soon)
JavaScript SDK
Amazon S3
bucket
Server-Side SDKs
Offline data
Amazon
Personalize

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Deliver personalized recommendations in production
Web
Application
(Serverless/
Containers/E
C2/etc..)
Browser /
Application
Inference query
Recommendations
Recommendation request
Recommendations
Real-time event data
Amazon
Personalize

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Console to monitor and track all the different steps

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Demo
Web
Application
Browser /
Application
Inference query
Recommendations
Recommendation
requestRecommendations
Real-time event data
Amazon
Personalize

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Amazon Forecast
Accurate time-series forecasting service, based
on the same technology used at Amazon.com.
No ML Experience Required

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Predicts spikes
accurately!
Generates forecasts
for new items
Learns relationships
between multiple
related time-series
Incorporates external
data (holidays,
promotions, and
so on)
Amazon Forecast solves hard problems in forecasting

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Historical data
Sales, inventory, pricing, etc.
Related data
Weather, competitive promotions, etc.
1. Load data
2. Inspect data
3. Identify features
4. Select algorithms
5. Select hyperparameters
6. Train models
7. Optimize models
8. Deploy and host models
Amazon Forecast
Customized
Forecasting
API
Private
Amazon Forecast - How it works

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• Amazon Forecast is applicable across
multiple domains
• You can set your domain using the
console or via the API
• You upload datasets with different
schemas based on the domain
Applicable across multiple domains

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There are three types of datasets in Amazon Forecast:
Item metadataTarget time-series Related time-series
Related time-
series such as
price, web-hits
etc.
Historic time-
series data of
items to
forecast
Attributes of
the item such
as category,
genre and
brand
Amazon Forecast data ingestions

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Diving a little deeper…

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Key metrics reported by Amazon Forecast
Amazon Forecast creates probabilistic forecasts (that is, forecasts for
specific prediction intervals)

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Amazon Forecast – Algorithms
Use AutoML or pick a
pre-defined algorithm
• Choose a pre-existing
algorithm (packaged as
Personalize Recipes) or
• AutoML and Forecast will
pick the right recipe for
you
• ARIMA

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How to add Amazon Forecast to your Application
Evaluate accuracy
metrics and deploy
to production.
Visualize results in
the console, or
export forecasts.
Import data from
Amazon S3 buckets.
Automatically
select algorithms
through the API or
on the console, or
choose your own.

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Demo
Data
Scientist Amazon SageMaker
Jupyter
Notebook
Amazon Forecast

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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
The Amazon ML Stack: Broadest & Deepest Set of Capabilities
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
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
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization ( N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Models without training data (REINFORCEMENT LEARNING)Algorithms & models ( A W S M A R K E T P L A C E )
Language Forecasting Recommendations
NEW NEWNEW
NEW
NEW
NEWNEW
NEW
NEW

20. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Ben Snively
snivelyb@amazon.com