AWS Machine Learning Explained (with examples)
Source: AWS Published: November 2019
Source: AWS
Published: November 2019
AWS Machine Learning Explained (with examples)
Circulated: December 16, 2019
SageMaker: Build, train, and deploy machine learning models
Comprehend: Find insights and relationships in text
Example: find negative customer interactions with customer service agents
Forecast: Highly accurate time-series forecasts
Example: predict the future sales of a raincoat by looking only at its previous sales data
Lex: Convert speech to text and recognize the intent
Example: when Alexa listens to your request
Personalize: Individualize recommendations for customers
Example: Amazon.com product recommendations
Polly: Convert text to speech and sound like a human voice
Example: when Alexa responds to your request
Rekognition: Identify objects, people, text, scenes, and activities in images and videos
Example: get a person’s predicted gender, age range, glasses, and facial hair from an image
Textract: Extract text from scanned documents
Example: digitize paper healthcare records
Transcribe: Turn an audio file into a text file
Example: generate captions to display with a video
Translate: Translate text between languages
Example: make product reviews accessible to global customers