diff --git a/README.md b/README.md index a0a655f..01e0bd2 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ - [Cloud Monitoring](sections/cloud_monitoring.md) - [VPC](sections/vpc.md) - [Security & Compliance](sections/Security-Compliance.md) +- [Machine Learning](sections/machine-learning.md) ### Contributors diff --git a/sections/machine-learning.md b/sections/machine-learning.md new file mode 100644 index 0000000..08cd9af --- /dev/null +++ b/sections/machine-learning.md @@ -0,0 +1,135 @@ +# 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 +- + +## 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 \ No newline at end of file