AWS-CCP-Notes/sections/machine_learning.md

# Machine Learning

- [Machine Learning](#machine-learning)
  - [Amazon Rekognition](#amazon-rekognition)
  - [Amazon Transcribe](#amazon-transcribe)
  - [Amazon Polly](#amazon-polly)
  - [Amazon Translate](#amazon-translate)
  - [Amazon Lex & Connect](#amazon-lex--connect)
    - [Amazon Lex: (same technology that powers Alexa)](#amazon-lex-same-technology-that-powers-alexa)
    - [Amazon Connect](#amazon-connect)
  - [Amazon Comprehend](#amazon-comprehend)
  - [Amazon SageMaker](#amazon-sagemaker)
  - [Amazon Forecast](#amazon-forecast)
  - [Amazon Kendra](#amazon-kendra)
  - [Amazon Personalize](#amazon-personalize)
  - [Amazon Textract](#amazon-textract)
  - [Summary](#summary)

## Amazon Rekognition

- Find **objects, people, text, scenes** in **images and videos** using ML
- Facial analysis and facial search to do user verification, people counting
- Create a database of “familiar faces” or compare against celebrities
- Use cases:
- Labeling
  - Content Moderation
  - Text Detection
  - Face Detection and Analysis (gender, age range, emotions…)
  - Face Search and Verification
  - Celebrity Recognition
- <https://aws.amazon.com/rekognition/>

## Amazon Transcribe

- Automatically **convert speech to text**
- Uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately
- Use cases:
  - transcribe customer service calls
  - automate closed captioning and subtitling
  - generate metadata for media assets to create a fully searchable archive

## Amazon Polly

- Turn **text into lifelike speech** using deep learning
- Allowing you to create applications that talk

## Amazon Translate

- Natural and accurate **language translation**
- Amazon Translate allows you to localize content - such as websites and applications - for international users, and to easily translate large volumes of text efficiently.

## Amazon 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 text, callers
- Helps build chatbot, 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 payments, 80% cheaper than traditional contact center solutions

## Amazon Comprehend

- For **Natural Language Processing – NLP**
- Fully managed and serverless service
- Uses machine learning to find insights and relationships in text
  - Language of the text
  - Extracts key phrases, places, people, brands, or events
  - Understands how positive or negative the text is
  - Analyzes text using tokenization and parts of speech
  - Automatically organizes a collection of text files by topic
- Sample use cases:
  - analyze customer interactions (emails) to find what leads to a positive or negative experience
  - Create and groups articles by topics that Comprehend will uncover

## Amazon SageMaker

- Fully managed service for **developers / data scientists to build ML models**
- Typically, difficult to do all the processes in one place + provision servers
- Machine learning process (simplified): predicting your exam score

## Amazon Forecast

- Fully managed service that uses ML to deliver highly accurate forecasts
- Example: predict the future sales of a raincoat
- 50% more accurate than looking at the data itself
- Reduce forecasting time from months to hours
- Use cases: Product Demand Planning, Financial Planning, Resource Planning,etc..

## Amazon Kendra

- Fully managed document search service powered by Machine Learning
- Extract answers from within a document (text, pdf, HTML, PowerPoint, MS Word, FAQs…)
- Natural language search capabilities
- Learn from user interactions/feedback to promote preferred results (Incremental Learning)
- Ability to manually fine-tune search results (importance of data, freshness, custom,etc..)

## Amazon Personalize

- Fully managed ML-service to build apps with real-time personalized recommendations
- Example: personalized product recommendations/re-ranking, customized direct marketing
  - Example: User bought gardening tools, provide recommendations on the next one to buy
- Same technology used by Amazon.com
- Integrates into existing websites, applications, SMS, email marketing systems, …
- Implement in days, not months (you don’t need to build, train, and deploy ML solutions)
- Use cases: retail stores, media and entertainment

## Amazon Textract

- Automatically extracts text, handwriting, and data from any scanned documents using AI and ML
- Extract data from forms and tables
- Read and process any type of document (PDFs, images, …)
- Use cases:
  - Financial Services (e.g., invoices, financial reports)
  - Healthcare (e.g., medical records, insurance claims)
  - Public Sector (e.g., tax forms, ID documents, passports)

## Summary

- Rekognition: face detection, labeling, celebrity recognition
- Transcribe: audio to text (ex: subtitles)
- Polly: text to audio
- Translate: translations
- Lex: build conversational bots – chatbot
- Connect: cloud contact center
- Comprehend: natural language processing
- SageMaker: machine learning for every developer and data scientist
- Forecast: build highly accurate forecasts
- Kendra: ML-powered search engine
- Personalize: real-time personalized recommendations
- Textract: detect text and data in documents

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