AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
21. Extend capabilities with the help of an
open ecosystem!
Amazon
Echo
Alexa Skill AWS Lambda External API Service
22. AW S DEEP LEARNING AMI
Apache
MXNet
TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
VISION LANGUAGE
Amazon Rekognition
Image
Amazon
Comprehend
Amazon Lex
Amazon Rekognition
Video
AWS DeepLensAmazon SageMaker Spark and EMR
Amazon Polly
Amazon
Translate
Amazon
Transcribe
The Amazon machine learning stack
23. Amazon Transcribe: Automatic Speech Recognition
Time stamps and
confidence scores
Support for both
regular and
telephony audio
Punctuation
§
Detect multiple
speakers
Custom
vocabulary
25. Amazon.com, Inc. is located in Seattle, WA
and was founded July 5th, 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
Text Analysis
26. Call center insights
Transcribe 8Khz call
recordings with
high accuracy
Analyze the text with
Amazon Comprehend
Visualize results
on Amazon
QuickSight
Amazon
Transcribe
Amazon
Comprehend
Amazon
Connect
Amazon
Quicksight
27. Amazon Lex
Conversational interfaces for your
applications, powered by the same
Natural Language Understanding
(NLU) & Automatic Speech
Recognition (ASR) models as Alexa
Voice and text
“chatbots”
Integrates with
call centers
Voice interactions
on mobile, web,
and devices
Text interaction
with Slack, Twilio SMS, Kik,
Facebook Messenger
Enterprise
connectors
28. Intents
A particular goal that
the user wants to
achieve
Utterances
Spoken or typed phrases
that invoke your intent
Slots
Data the user must provide to fulfill the
intent
Prompts
Questions that ask the user to
input data
Fulfillment
The business logic required to fulfill the
user’s intent
BookHotel
29. Amazon Rekognition
Object and Scene
Detection
Facial
Analysis
Face
Recognition
Text in Image
Deep learning-based computer vision service for images and video
Unsafe Image
Detection
Celebrity
Recognition
36. A fully managed service that enables data scientists and
developers to quickly and easily build machine-learning based
models into production smart applications.
Amazon SageMaker
End-to-End Machine
Learning Platform
In-Built ML
Algorithms
Flexible Model
Training
Pay by the second
38. Deploy model
in production
Train and tune
models
Setup &
Manage
environment
for training
Choose &
optimise your
ML model
Scale and
manage the
production
environment
Amazon SageMaker
Pre-built
notebooks for
common
problems
BUILD
44. MXNet & TensorFlow
SDK
TensorFlow SDK
MXNet (Gluon) SDK
SageMaker Built-in
Algorithms
K-means Clustering
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner –
Classification
DeepAR Forecasting
Bring Your
Own
Algorithms
ML Algorithms
R
MXNet
TensorFlow
Caffe
PyTorch
Keras
CNTK
…
Apache Spark
Estimator
Apache Spark Python library
Apache Spark Scala library
Training Using Amazon SageMaker
Amazon
EMR
45. Amazon SageMaker
TRAIN
Deploy model
in production
Scale and
manage the
production
environment
Pre-built
notebooks for
common
problems
BUILD
Built-in, High
performance
Algorithms
One-click
Training
Hyperparameter
Optimisation
46. Amazon SageMaker
One-click
Deployment
Scale and
manage the
production
environment
DEPLOY
Pre-built
notebooks for
common
problems
Built-in, High
performance
Algorithms
One-click
Training
Hyperparameter
Optimisation
BUILD TRAIN
47. Train model in the cloud Run model at the edge
AWS
Greengrass
AWS IoT
Tesla V100
120 TFLOPS
Amazon
EC2
48. Amazon SageMaker
Fully managed
hosting with
Auto-scaling
DEPLOY
One-click
Deployment
One-click
Deployment
Pre-built
notebooks for
common
problems
Built-in, High
performance
Algorithms
One-click
Training
Hyperparameter
Optimisation
BUILD TRAIN
50. Join us to learn how Amazon and pioneering
businesses build a culture which drives
innovation at scale. Register Today!
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