Introduction to AI/ML with AWS

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Suman Debnath
Principal Technical Evangelist
Amazon Web Service
Introduction to Machine Learning
@_sumand debnsuma

Put machine learning in the
hands of every developer
Our mission at

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
Reinforcement learningAlgorithms & models ( A W S M A R K E T P L A C E
F O R M A C H I N E L E A R N I N G )
Language Forecasting Recommendations

AI Services
Pre-trained AI services that require
no ML skills or training
Easily add intelligence to your
existing apps and workflows
Quality and accuracy from
continuously-learning APIs
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
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
Language Forecasting Recommendations

Audio Input
Example: End-to-end audio analysis
store
Amazon S3
trigger
AWS
Lambda
call
Amazon
Transcribe
in cascade
Amazon
Comprehend
aggregate
Amazon
Athena
analyze
Amazon
QuickSight

Machine Learning APIs for : Vision

Amazon Rekognition – Image and Video Analysis

Optical Character Recognition (OCR)

Object & Scene Detection

Facial Analysis

Face Search/Comparison

Machine Learning APIs for : Chatbots

Amazon Lex
A service for building conversational
interfaces into your applications using
voice and text

Amazon Lex Bots – key concepts
Utterances
Spoken or typed phrases that invoke
your intent
BookHotel
Intents
An intent performs an action in response
to natural language user input
Slots
Slots are input data required to fulfill
the intent
Fulfillment
Fulfillment mechanism for your intent

Machine Learning APIs for : Speech

Amazon Polly
Turn text into lifelike speech using deep learning

Amazon Polly
• Content creation
• Mobile & desktop applications
• Internet of Things (IoT)
• Education & e-learning
• Telephony
• Game development
Use cases
• 58 voices across 28 languages
• Lip-syncing & text highlighting
• Fine-grained voice control
• Custom vocabularies
• Available in 18 AWS Regions
Key features

Synthesize Speech API
$ aws polly synthesize-speech
--text "hello"
--voice-id Suman
--output-format mp3
[--lexicon-names mylex1 mylex2]
output.mp3
{
"ContentType": "audio/mpeg",
"RequestCharacters": "11"
}

Amazon Transcribe
Turn speech into text

“Hello, this is Allan
speaking”
Amazon
Transcribe
Speech-to-text (STT)

Machine Learning APIs for : Language

How do you extract insights from
unstructured text?

A fully managed and continuously
trained service that helps you extract
insights from unstructured text
Amazon Comprehend

Amazon Comprehend
Sentiment Entities LanguagesKeyphrases
Topic
modeling
Syntax

Amazon Comprehend – Natural Language Processing
Amazon.com, Inc. is located in Seattle, WA
and was founded July 5, 1994 by Jeff
Bezos. Our customers love buying
everything from books to blenders at
great prices
Named Entities
Amazon.com: Organization
Seattle, WA : Location
July 5th,1994: Date
Jeff Bezos : Person
Keyphrases
Our customers
books
blenders
great prices
Sentiment
Positive
Language
English

Sentiment Analysis
$ aws comprehend detect-sentiment
--language-code 'en' --text 'I love cloud!’
{
"Sentiment": "POSITIVE”,
"SentimentScore": {
"Mixed": 0.012617903761565685,
"Positive": 0.9599817991256714,
"Neutral": 0.021758323535323143,
"Negative": 0.005641999188810587
}
}

Amazon Translate
Yes, natural language translation J

Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
Collect and prepare
training data
Choose and
optimize your
ML algorithm
Train and
Tune ML Models
Set up and
manage
environments
for training
Deploy models
in production
Scale and manage
the production
environment
1
2
3

Machine learning cycle
Business
Problem
ML problem
framing
Data collection
Data integration
Data preparation
and cleaning
Data visualization
and analysis
Feature
engineering
Model training and
parameter tuning
Model evaluation
Monitoring and
debugging
Model deployment
Predictions
Are
business
goals
met?
YESNO
Dataaugmentation
Feature
augmentation
Re-training

Manage data on AWS
Business
Problem
ML problem
framing
Data collection
Data integration
Data preparation
and cleaning
Data visualization
and analysis
Feature
engineering
Model training and
parameter tuning
Model evaluation
Monitoring and
debugging
Model deployment
Predictions
Are
business
goals
met?
YESNO
Dataaugmentation
Feature
augmentation
Re-training

Build and train models using SageMaker
Business
Problem
ML problem
framing
Data collection
Data integration
Data preparation
and cleaning
Data visualization
and analysis
Feature
engineering
Model training and
parameter tuning
Model evaluation
Monitoring and
debugging
Model deployment
Predictions
Are
business
goals
met?
YESNO
Dataaugmentation
Feature
augmentation
Re-training

Deploy models using SageMaker
Business
Problem
ML problem
framing
Data collection
Data integration
Data preparation
and cleaning
Data visualization
and analysis
Feature
engineering
Model training and
parameter tuning
Model evaluation
Monitoring and
debugging
Model deployment
Predictions
Are
business
goals
met?
YESNO
Dataaugmentation
Feature
augmentation
Re-training

Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high-
performance
algorithms
and frameworks
One-click
training
Hyperparameter
optimization
DeployTrainBuild
Model compilation
Elastic inference
Inference pipelines
P3DN, C5N
TensorFlow on 256 GPUs
Resume HPO tuning job
New built-in algorithms
scikit-learn environment
Model marketplace
Search
Git integration
Elastic inference

Machine Learning Marketplace

The Amazon SageMaker API
• Python SDK orchestrating all Amazon SageMaker activity
• High-level objects for algorithm selection, training, deploying,
automatic model tuning, etc.
• Spark SDK (Python & Scala)
• AWS CLI: ‘aws sagemaker’
• AWS SDK: boto3, etc.

Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
GroundTruth
Client application
Inference code
Training code
Inference requestInference response
Inference Endpoint

Training code
Factorization Machines
Linear Learner
Principal Component Analysis
K-Means Clustering
XGBoost
And more
Built-in Algorithms Bring Your Own ContainerBring Your Own Script
Model options

Thank you !
Suman Debnath
Principal Technical Evangelist
Amazon Web Service
@_sumand debnsuma

Introduction to AI/ML with AWS

Introduction to AI/ML with AWS

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