Building Text Analytics Applications on AWS using Amazon Comprehend - AWS Online Tech Talks

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Learning Objectives: - Get an introduction to Natural Language Processing (NLP) - Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep - Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Nino Bice, Product Manager
Ben Snively, Service Architect
Amazon Comprehend
Fully Managed, Continuously Trained NLP Service

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Frameworks
&
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow Gluon
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens
Amazon confidential
AWS ML Stack

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Transcribe
Automatic conversion of speech into accurate, grammatically
correct text
Amazon Translate Natural and fluent language translation
Amazon Polly Turn text into lifelike speech using deep learning
Amazon Comprehend Discover insights and relationships in text
Amazon Lex
Conversational interfaces for text-based and voice-based
applications
Powerful language capabilities

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Comprehend
A fully managed and continuously trained service that
helps you extract insights from unstructured text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Comprehend
Sentiment Entities LanguagesKey phrases Topic modeling

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Comprehend: Natural Language Processing
A m a z o n . c o m , I n c . i s l o c a t e d i n
S e a t t l e , WA a n d w a s f o u n d e d
J u l y 5 t h , 1 9 9 4 b y J e f f B e z o s .
O u r c u s t o m e r s l o v e b u y i n g
e v e r y t h i n g f r o m b o o k s t o
b l e n d e r s a t g r e a t p r i c e s
N a m e d E n t i t i e s
• A m a z o n . c o m :
O r g a n i z a t i o n
• S e a t t l e , W A : L o c a t i o n
• J u l y 5 t h , 1 9 9 4 : D a t e
• J e f f B e z o s : P e r s o n
K e y p h r a s e s
• O u r c u s t o m e r s
• b o o k s
• b l e n d e r s
• g r e a t p r i c e s
S e n t i m e n t
• P o s i t i v e
L a n g u a g e
• E n g l i s h

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Topic Modeling
Document Topic Proportion
Doc.txt 0 .89
Doc.txt 1 .67
Doc.txt 2 .91
Topic Term Weight
0 Washington .89
1 Silicon Valley .67
2 Roasting .91
Keywords Topic Groups Document Relationship to Topics

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data
Collection
Web
Crawling
Internal
Data
Warehouse
Purchase
Datasets
Annotation
Orchestration
Data
Cleansing
Annotation Evaluation
Model
Training
Build Experiment Optimize
Deploy Dockerize Deploy Host
Continuous
Improvement
Sample
Data
Annotate
and
Evaluate
Repeat
Steps 3 - 5
1.
2.
3.
4.
5.
Continuously Training

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Comprehend Portal

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Text Analytics

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Popular Text Analytics Use Cases
Content Personalization: Customers are using Comprehend NLP output to understand related documents
based on entities, phrases or even topic similarities for trends analysis, to drive content personalization and
recommendations
Semantic Search: Customers using Amazon Comprehend to index entities for boosting and ranking
search results.
Intelligent data warehouse: Customers are using Amazon Comprehend to query unstructured data in
relational databases, processing data within the data lake (S3) and then inserting it back into the data
warehouse
Social Analytics: Customers are using Amazon Comprehend to ingest, process and analyze trends from
entities and sentiment from social media posts across Twitter and Facebook.

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Text Analytics Workload
Amazon Kinesis
Amazon ES
Amazon Redshift
Amazon EMR
• Semantic
• Rich Filtering
• Grouping, Trends
• Joining, Correlating
• Clustering
• Graph, Search
• Near real-time
• Alerts
Amazon S3
Social Media, Support
Amazon Aurora
Articles, Documents

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Comprehend and Glue

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
UDF to Call Comprehend

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Comprehend and RDS

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Stored Procedure to Comprehend

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Demo: Comprehend and RDS

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Comprehend and Elasticsearch

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Comprehend and SageMaker

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

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La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup. Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti. Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.

Esegui pod serverless con Amazon EKS e AWS Fargate

Amazon Web Services

Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi

Costruire Applicazioni Moderne con AWS

Amazon Web Services

Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.

Come spendere fino al 90% in meno con i container e le istanze spot

Amazon Web Services

L’utilizzo dei container è in continua crescita. Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili. I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!

Open banking as a service

Amazon Web Services

In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th. Event Agenda : Open banking so far (short recap) • PSD2, OB UK, OB Australia, OB LATAM, OB Israel Intro to Open Finance marketplace • Scope • Features • Tech overview and Demo The role of the Cloud The Future of APIs • Complying with regulation • Monetizing data / APIs • Business models • Time to market One platform for all: a Strategic approach Q&A

Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...

Amazon Web Services

Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc. AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta. Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.

OpsWorks Configuration Management: automatizza la gestione e i deployment del...

Amazon Web Services

Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity. AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet. Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.

Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads

Amazon Web Services

Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.

Computer Vision con AWS

Amazon Web Services

Database Oracle e VMware Cloud on AWS i miti da sfatare

Amazon Web Services

Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti. Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.

Crea la tua prima serverless ledger-based app con QLDB e NodeJS

Amazon Web Services

Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti. Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire. Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito. In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.

API moderne real-time per applicazioni mobili e web

Amazon Web Services

Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline. Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.

Database Oracle e VMware Cloud™ on AWS: i miti da sfatare

Amazon Web Services

Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali. In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.

Tools for building your MVP on AWS

Amazon Web Services

1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS). 2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels. 3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.

How to Build a Winning Pitch Deck

Amazon Web Services

This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.

Building a web application without servers

Amazon Web Services

This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.

Fundraising Essentials

Amazon Web Services

This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.

AWS_HK_StartupDay_Building Interactive websites while automating for efficien...

Amazon Web Services

This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.

Introduzione a Amazon Elastic Container Service

Amazon Web Services

Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.

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Building Text Analytics Applications on AWS using Amazon Comprehend - AWS Online Tech Talks

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Frameworks & Infrastructure AWS Deep Learning AMI GPU (P3 Instances) MobileCPU IoT (Greengrass) Vision: Rekognition Image Rekognition Video Speech: Polly Transcribe Language: Lex Translate Comprehend Apache MXNet PyTorch Cognitive Toolkit Keras Caffe2 & Caffe TensorFlow Gluon Application Services Platform Services Amazon Machine Learning Mechanical Turk Spark & EMR Amazon SageMaker AWS DeepLens Amazon confidential AWS ML Stack

3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Transcribe Automatic conversion of speech into accurate, grammatically correct text Amazon Translate Natural and fluent language translation Amazon Polly Turn text into lifelike speech using deep learning Amazon Comprehend Discover insights and relationships in text Amazon Lex Conversational interfaces for text-based and voice-based applications Powerful language capabilities

6. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Comprehend: Natural Language Processing A m a z o n . c o m , I n c . i s l o c a t e d i n S e a t t l e , WA a n d w a s f o u n d e d J u l y 5 t h , 1 9 9 4 b y J e f f B e z o s . O u r c u s t o m e r s l o v e b u y i n g e v e r y t h i n g f r o m b o o k s t o b l e n d e r s a t g r e a t p r i c e s N a m e d E n t i t i e s • A m a z o n . c o m : O r g a n i z a t i o n • S e a t t l e , W A : L o c a t i o n • J u l y 5 t h , 1 9 9 4 : D a t e • J e f f B e z o s : P e r s o n K e y p h r a s e s • O u r c u s t o m e r s • b o o k s • b l e n d e r s • g r e a t p r i c e s S e n t i m e n t • P o s i t i v e L a n g u a g e • E n g l i s h

7. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Topic Modeling Document Topic Proportion Doc.txt 0 .89 Doc.txt 1 .67 Doc.txt 2 .91 Topic Term Weight 0 Washington .89 1 Silicon Valley .67 2 Roasting .91 Keywords Topic Groups Document Relationship to Topics

8. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Collection Web Crawling Internal Data Warehouse Purchase Datasets Annotation Orchestration Data Cleansing Annotation Evaluation Model Training Build Experiment Optimize Deploy Dockerize Deploy Host Continuous Improvement Sample Data Annotate and Evaluate Repeat Steps 3 - 5 1. 2. 3. 4. 5. Continuously Training

11. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Popular Text Analytics Use Cases Content Personalization: Customers are using Comprehend NLP output to understand related documents based on entities, phrases or even topic similarities for trends analysis, to drive content personalization and recommendations Semantic Search: Customers using Amazon Comprehend to index entities for boosting and ranking search results. Intelligent data warehouse: Customers are using Amazon Comprehend to query unstructured data in relational databases, processing data within the data lake (S3) and then inserting it back into the data warehouse Social Analytics: Customers are using Amazon Comprehend to ingest, process and analyze trends from entities and sentiment from social media posts across Twitter and Facebook.

12. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Text Analytics Workload Amazon Kinesis Amazon ES Amazon Redshift Amazon EMR • Semantic • Rich Filtering • Grouping, Trends • Joining, Correlating • Clustering • Graph, Search • Near real-time • Alerts Amazon S3 Social Media, Support Amazon Aurora Articles, Documents

Building Text Analytics Applications on AWS using Amazon Comprehend - AWS Online Tech Talks

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