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