AWS Machine Learning
Amazon Comprehend
- Natural Language Processing (NLP)
- Input = Document
- Output = Entities, phrases, language, PII, sentiments
- Pre-trained models or custom
- Real-time analysis for small workloads
- Async jobs for larger workloads
- Console, CLI for interactive or use APIs to build into applications
Amazon Kendra
- Intelligent search service designed to mimic interacting with human expert
- Supports wide range of question types
- Factoid: Who, What & Where
- Descriptive: How do I ..?
- Keyword: What day is the keynote address?
- Index: searchable data organized in an efficient way
- Data source: Where your data lives. Kendra connects and indexes from this location
- Synchronize with index based on a schedule
- Integrates with AWS Services (IAM, Identity Center)
Amazon Lex
- Text or Voice consersational interfaces
- Powers the Alexa service
- Automatic speech recognition (ASR) - speech to text
- Natural Language Understanding (NLU) - Intent
- Build understanding into you application
- Scales, integrates, Quick to deploy, pay as you go pricing
- Chatbots, voice assistants, Q&A bots, Info/Enterprise Bots
- Lex can fulfill the intent using lambda integration
Amazon Polly
- Converts text into "life-like" speech
- Text (language) => Speech (language) NO TRANSLATION
- Modes: Standard TTS
- Modes: Neural TTS
- Can use Speech Synthesis Markup Language (SSML): Additional control over how polly generates speech (emphasis, pronunciation, whispering)
Amazon Rekognition
- Deep learning image & video analysis
- Identify object, people, text, activities, content moderation, face detection, face analysis, face comparison, pathing & much more
- Pay per image or per minute video
- Integrates with application & event driven
- Can analyse live video streams - kinesis video streams
- Detect and Analyse text contained in input documents
- Input = JPEG, PNG, PDF or TIFF
- Output = Extracted text, structure & Analysis
- Synchronous (real-time) for small documents and Asynchronous for large files
- Pay per usage. Custom pricing available for large volume
- Use case:
- Detection of text & relationship between text
- generate metadata
- Document analsysis (names,addresses, birthdate)
- Receipt analysis (prices, vendor, line-items, dates)
- Identity documents (document ID)
Amazon Transcribe
- Automatic Speech Recognition (ASR) service
- Input=Audio, Output=Text
- Language customization, filters for privacy, audience-appropriation language, speaker identification
- Custom vocabularies and language models
- Pay as you use per second of transcribed audio.
- Use case:
- Full text indexing of audio - allow searching
- Meeting notes
- Subtitles/Captions & transcripts
- Call analytics(characteristics, summarization, categories and sentiment)
- Integration wiht other apps / AWS ML services
Amazon Translate
- Text translation service based on ML
- Translates text from native language to other languages. One word at a time.
- Encoder reads source => semantic representation (meaning)
- Decoder reads meaning => writes target language
- Attention mechanism ensures 'meaning' is translated
- Auto detect source text language
- Use case:
- Multilingual user experience
- Translate incoming data (social media, news, communications)
- Language independence for other AWS services (Comprehend, transcribe, polly, data stored in S3, RDS, Database)
Amazon Forecast
- Forecasting for time-series data
- Retail demand, supply chain, staffing, energy, server capacity, web traffic
- Import historical & related data, Output forecast and forecast explainability
- Web Console (visualization), CLI, APIs, Python SDK
Amazon Fraud Detector
- Fully managed Fraud detection service
- New account creations, payments, guest checkout
- Upload historical data, choose model type
- Model Types:
- Online Fraud - Little historical data eg: new customer account
- Transaction Fraud - transactional history, idenitfying suspect payments
- Acount Takeover - Identify phishing or another social based attack
- All events are scored. Rules/Detection logic allow you to react to a score based on business activity
Amazon SageMaker
- Fully managed Machine Learning (ML) service
- Fetch, Clean, Prepare, Train, Evaluate, Deploy, Monitor/Collect
- Sage Maker Studio: Build, train, debug and monitor models - IDE for ML lifecycle
- Sagemaker Domain - EFS Volume, Users, Apps, Policies, VPCs -- isolation
- Container - Docker containers deployed to ML EC2 instance - ML environments (OS, libraries, tooling)
- Hosting - Deploy endpoints for your models
- Sagemaker has no cost - the resources it creates have a cost (Complex pricing)