Amazon Rekognition #

Finding objects, people, text, scenes in images and videos using Machine Learning. #

Facial analysis and facial search to perform user verification, people count, etc.

• Use cases:
  • Labeling
  • Content Moderation
  • Text Detection
  • Face Detection and Analysis (gender, age, range, emotions, …)
  • Face Search and Verification
  • Celebrity Recognition
  • Pathing (e.g. sports game analysis)

More about Rekognition: https://aws.amazon.com/rekognition

Transcribe #

Automatically converts speech to text. Uses deep learning process called ASR (Automatic Speech Recognition) to convert speech to text. #

It has a capability of removing Personally Identifiable information (PII) using Redaction.

It also supports Automatic Language Identification for multi-lingual audio.

• Use cases:
  • transcribe customer service calls
  • automate closed captioning and subtitling
  • generate metadata for media assets to create a fully searchable archive

Polly #

Opposite of Transcribe. Turns text into speech using deep learning. #

Translate #

Amazon Translate allows localizing the content - websites and applications - for international users. It has a capability of translating large volumes of text efficiently.

Lex & Connect #

• Amazon Lex: same technology that powers Alexa
  • Automatic Speech Recognition (ASR) to convert speech to text
  • Natural Language Understanding to recognize the intent of the text
  • Helps building chatbots or call center bots
• Amazon Connect
  • Receive calls, create contact flows, cloud-based virtual contact center
  • Can integrate with other CRM systems or AWS
  • No upfront payment, 80% cheaper than traditional contact center solutions

Comprehend #

Uses Machine Learning to find insights and relationships in the text for Natural Language Processing - NLP. #

• Language of the text
• Extracting key phrases, places, people, brands or events
• Understand how positive or negative text is
• Analyzes text using tokenization and parts of speech
• Automatically organizes a collection of text files by topic

Use cases:

• Analyze customer interactions (emails) to find what leads to a positive or negative experience
• Create and group articles by topics

SageMaker #

Fully managed service for developers / data scientists to build Machine Learning (ML) models. Model requires training.

Kendra #

Fully managed document search service powered by Machine Learning.

• Can extract answers from withing a document (text, pdf, HTML, Power Point, MS Word, FAQs, etc.).
• Can learn from user interactions or feedback to promote preferred results (Incremental Learning)
• Has an ability to manually fine-tune search results (importance of data, freshness, custom, etc.)

Personalize #

Fully managed Machine Learning service to build apps with real-time personalized recommendations.

Same tech used by amazon.com

Use Cases: retail stores, media, entertainment…

Textract #

Automatically extracts text, handwriting and data from scanned documents using AI and ML.

Can read and process any type of document (PDF, images, etc.)

Summary #

• Rekognition: face detection, labeling, celebrity recognition
• Transcribe: audio to text (e.g. subtitles)
• Polly: text to audio
• Translate: translations
• Lex: build conversational bots / chatbots
• Connect: cloud contact center
• Comprehend: natural language processing
• SageMaker: machine learning for every developer and data scientist
• Kendra: ML-powered document search engine
• Personalize: real-time personalized recommendation
• Textract: detect text and data in documents (handwriting / scanned data)

» Sources « #

• Amazon Rekognition: https://aws.amazon.com/rekognition

» Table of contents (CLF-C02) « #

» Disclaimer « #