[Modify/Add] Add more doc. and some typo fix
This commit is contained in:
138
sections/machine_learning.md
Normal file
138
sections/machine_learning.md
Normal file
@@ -0,0 +1,138 @@
|
||||
# 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
|
||||
|
||||
- Identifies objects, people, text, and scenes in images and videos using ML
|
||||
- Facial analysis and facial search for user verification and people counting
|
||||
- Create a database of familiar faces or compare against celebrities
|
||||
- Key uses:
|
||||
- Labeling
|
||||
- Content Moderation
|
||||
- Text Detection
|
||||
- Face Detection and Analysis (gender, age range, emotions)
|
||||
- Face Search and Verification
|
||||
- Celebrity Recognition
|
||||
- More info: [Amazon Rekognition](https://aws.amazon.com/rekognition/)
|
||||
|
||||
## Amazon Transcribe
|
||||
|
||||
- Converts speech to text using Automatically speech recognition (ASR)
|
||||
- Accurate and quick transcription
|
||||
- Key uses:
|
||||
- Transcribing customer service calls
|
||||
- Automating closed captioning and subtitling
|
||||
- Generating metadata for media assets for searchable archives
|
||||
|
||||
## Amazon Polly
|
||||
|
||||
- Turns text into lifelike speech using deep learning
|
||||
- Enables creation of talking applications
|
||||
|
||||
## Amazon Translate
|
||||
|
||||
- Provides natural and accurate language translation
|
||||
- Localizes content for international users
|
||||
- Efficiently translates large volumes of text
|
||||
|
||||
## Amazon Lex & Connect
|
||||
|
||||
### Amazon Lex: (same technology that powers Alexa)
|
||||
|
||||
- Uses ASR to convert speech to text
|
||||
- Natural Language Understanding to recognize text and caller intent
|
||||
- Helps build chatbots and call center bots
|
||||
|
||||
### Amazon Connect
|
||||
|
||||
- Cloud-based virtual contact center
|
||||
- Receives calls and creates contact flows
|
||||
- Integrates with CRM systems or AWS
|
||||
- No upfront payments, 80% cheaper than traditional contact centers
|
||||
|
||||
## Amazon Comprehend
|
||||
|
||||
- Fully managed and serverless NLP service
|
||||
- Uses machine learning to find insights and relationships in text
|
||||
- Key features:
|
||||
- Identifies language of the text
|
||||
- Extracts key phrases, places, people, brands, or events
|
||||
- Determines sentiment of the text
|
||||
- Analyzes text using tokenization and parts of speech
|
||||
- Organizes text files by topic
|
||||
- Key uses:
|
||||
- Analyzing customer interactions for positive or negative experiences
|
||||
- Grouping articles by topics
|
||||
|
||||
## 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
|
||||
- Reduces forecasting time from months to hours
|
||||
- Key uses:
|
||||
- Product Demand Planning
|
||||
- Financial Planning
|
||||
- Resource Planning
|
||||
|
||||
## Amazon Kendra
|
||||
|
||||
- Fully managed document search service powered by ML
|
||||
- Extracts answers from various document types (text, PDF, HTML, PowerPoint, MS Word, FAQs)
|
||||
- Offers natural language search capabilities
|
||||
- Learns from user interactions to promote preferred results
|
||||
- Allows manual fine-tuning of search results
|
||||
|
||||
## Amazon Personalize
|
||||
|
||||
- Fully managed ML service for real-time personalized recommendations
|
||||
- Key uses:
|
||||
- Personalized product recommendations
|
||||
- Customized direct marketing
|
||||
- Integrates into websites, applications, SMS, email marketing systems
|
||||
- Implemented in days, not months
|
||||
|
||||
## Amazon Textract
|
||||
|
||||
- Automatically extracts text, handwriting, and data from scanned documents using AI and ML
|
||||
- Reads and processes various document types (PDFs, images)
|
||||
- Key uses:
|
||||
- Financial Services (invoices, financial reports)
|
||||
- Healthcare (medical records, insurance claims)
|
||||
- Public Sector (tax forms, ID documents, passports)
|
||||
|
||||
## Summary
|
||||
|
||||
- **Rekognition**: Face detection, labeling, celebrity recognition
|
||||
- **Transcribe**: Audio to text (e.g., subtitles)
|
||||
- **Polly**: Text to audio
|
||||
- **Translate**: Language translation
|
||||
- **Lex**: Build conversational bots (chatbots)
|
||||
- **Connect**: Cloud contact center
|
||||
- **Comprehend**: Natural language processing
|
||||
- **SageMaker**: Machine learning for developers and data scientists
|
||||
- **Forecast**: Accurate forecasts
|
||||
- **Kendra**: ML-powered search engine
|
||||
- **Personalize**: Real-time personalized recommendations
|
||||
- **Textract**: Detect text and data in documents
|
||||
Reference in New Issue
Block a user