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AWS Application Services AI/ML

Rekognition

Fully managed service, serving a pre-trained deep learning model for image and video analysis.

  • Common to use AWS Lambda to connect to the services
  • Images: image moderation (eg. porn filter), facial analysis (find faces, gender), celebrity, comparison, text in image
  • Videos: Detect people of interest, create metadata catalog for stock videos, detect offensive content

Video architecture

  • Object is uploaded to S3
  • Triggers a lambda function to call the Rekognition service
  • Rekognition queries S3 to get the video and starts analysing
  • When done, will send a message through SNS topic that it's done
  • SNS will put that through an SQS
  • Another Lambda function triggered when the SNS topic is triggered, get metadata from SQS about the video results, and finally go to Rekognition and get the results

Amazon Polly

Text to speech service (TTS). Fully managed pre-trained deep learning. Supports multiple languages, male or female voices and custom lexicons for industry specific language.

  • Speech Synthesis Markup Language (SSML): Be more specific about the inflections (eg. whispering)
  • Use cases: Read out loud web content, provide generated announcements, automated voice response solutions

Amazon Transcribe

Speech to text. Automatic Speech Recognition (ASR). Pre-trained deep learning models, supports different languages and custom vocabulary

Amazon Translate

Text translation, fully managed pre-trained deep learning models. Batch process, real-time, custom terminology.

  • Use cases: Enhance online customer chat application to translate conversations in real time, batch translate documents, create news publish solutions to multiple languages
  • Knows more languages than other services, such as Comprehend

Amazon Comprehend

Natural language Processing (NLP) system. Give it text, get back analysis of the text.

  • Key Offerings: Key-phrase extraction, sentiment, syntax, entity recognition, language detection, topic modelling, multiple languages.
  • Medical named entity and relationship extraction
  • Customise: entities and classification
  • Use case: Customer sentiment analysis, label unstructured data, topics from transcribed audio

Amazon Lex

Chatbots, Alexa.

Service chaining w/ AWS Step Functions

Orchestrate multiple Lambda functions

Example architecture with Step functions

  • Audio is saved into an S3 Bucket
  • Lambda-1 is triggered, that simply calls the Step Function (red box)
  • Inside the Step Function, is a Lambda that calls both Transcribe & a Wait function (clock icon)
  • Transcribe then calls S3 to get back the audio file and starts processing it. This might take a while
  • The middle Lambda-2 has 1 role, to query Transcribe for the job status (eg. every 10seconds) and pass the status and data into a Decision Point (question mark)
  • If the status isn't finished the Decision Point invokes the Wait Function again, which restarts the check status process
  • If the status is finished, invoke the last Lambda-3 that invokes Comprehend
  • Comprehend analyses the data, sends the results back to Lambda-3 that passes it on to another service