Introduction to Machine Learning on AWS

Artificial intelligence is the larger field of study using computers to do processes generally done by the human brain.

Machine learning is a subset of artificial inteilligence where we work on algorithms that are able to learn from a set of training data.

Deep learning is a subset of machine learning that uses multilayer networks to build a model closer to a human brain.

All AI systems have three parts: 1) Training: supply training data to an algorithm 1) Model: Receive an output of the training process 1) Inference and hosting: use the model to make a prediction

All AWS services are exposed to each other via APIs that can be communicated via SDKs.

Amazon has a number of different ML services: 1) Amazon Rekognition: computer vision 1) Amazon Textract: automated text extraction and analysis 1) Amazon Comprehend: natural language processing 1) Amazon Transcribe: speech to text 1) Amazon Translate: machine langauge translation 1) Amazon Lex: chat bots and virtual agents 1) Amazon Sagemaker: build, train, and deploy your own machine learning models on managed infrastracture.

Amazon Rekegnition

  • Fully managed deep-learning image-recognition service.
  • Designed for scalability.
  • Capabilities: Recognizes scenes, objects, concepts, faces.

Key Features:

  • Uses pretrained models.
  • APIs provide inferences from these models.
  • Detects labels, faces, texts, and specific content like moderation labels and protective equipment in images.

API Examples:

  • CompareFaces: Matches faces between a source and target image.
  • DetectFaces: Identifies faces and provides details like age range, presence of smile, etc.
  • DetectLabels: Identifies objects, events, concepts, with a confidence score.
  • DetectModerationLabels: Finds potentially sensitive content.
  • DetectProtectiveEquipment: Identifies face, hand, and head covers.
  • DetectText: Recognizes text in an image.
  • Celebrity Recognition: Identifies celebrities in images.

Video Content Analysis:

  • Similar operations on stored video content.
  • Video-specific APIs for segment detection, person tracking, etc.

Collections of Faces APIs:

  • CreateCollection: Creates a collection of faces.
  • IndexFaces: Populates a collection with detected faces.
  • Search by Face ID or Image: Finds matching faces in a collection.

Custom Training and Custom Labels:

  • Ability to train Rekognition with user’s labeled datasets.
  • CreateDataset and DetectCustomLabels: User can create and use custom models for specific recognition tasks.

Applications:

  • Image search indexing (e.g., vacation rental photos with beach scenes).
  • Automating detection of missing parts in factory assembly lines.
  • Brand logo recognition in online videos.

Amazon Textract

  • Fully managed service for analyzing documents.
  • Extracts text and structure, including handwritten documents.
  • Built on AWS-trained models.

Available APIs:

  • AnalyzeDocument: Analyzes specific features like tables, forms, queries.
  • AnalyzeExpense: Processes expenses from receipts or invoices.
  • DetectDocumentText: Identifies text within a document.
  • AnalyzeID: Extracts information from identity documents like driver’s licenses.
  • Asynchronous operation: Start API (begins process, returns job ID), Get API (retrieves status/results).

API Responses:

  • AnalyzeDocument: Returns JSON data structure with blocks (page, line, word, key-value sets).
  • AnalyzeExpense: Identifies vendor, sales information from expenses.
  • DetectDocumentText: Provides blocks of text, lines, and words.
  • AnalyzeID: Extracts details from identity documents.

Practical Use Case:

  • Handwritten movie reviews: determine which API to use for extraction.
  • Focus on extracting text and structured data from documents.

Amazon Comprehend

  • Natural-language-processing machine learning service.
  • Extracts insights and connections from text.
  • Offers both pre-trained Amazon models and custom model training.

APIs for Language and Entity Detection:

  • Detect-Dominant-Language: Real-time and batch processing for language detection.
  • Batch-Detect-Dominant-Language: For batch analysis of up to 25 documents.
  • Start-Dominant-Language-Detection-Job: Asynchronous batch processing with S3 input/output.
  • Detect Named Entities: Identifies entities like persons, organizations in text.
  • Detect Key Noun Phrases: Finds important noun phrases in text.
  • Detect Personally Identifying Information: Identifies and redacts sensitive information.

Sentiment Analysis API:

  • Detect-Sentiment: Determines text sentiment (e.g., positive, negative).

Custom Model APIs:

  • Custom Classification: Classifies documents using custom labels.
  • Custom Entity Recognizer: Recognizes custom-defined entities in text.
  • Start-Document-Classification-Job and Start-Entities-Detection-Job: Asynchronous processing with custom models.
  • Classify-Document and Detect-Entities: Real-time processing with purchased endpoints for custom models.

Additional APIs and Features:

  • Event detection and topic detection.

  • Detailed AWS Documentation available for more information.

  • Practical Use Case Scenario:
    • Analyzing movie preview forms for audience sentiment.
    • Integrating sentiment analysis with demographic analytics.

    Amazon Transcribe

    • Converts speech to text.
    • Utilizes trained models for transcription subtleties.
    • Supports 37 languages (as of publishing).

Transcription Complexity:

  • Complex mapping from spoken word to text.
  • Examples: Currency and date formats.

Transcription APIs:

  • StartTranscriptionJob: Batch processing with media file input.
  • GetTranscriptionJob: Retrieves results and status of transcription jobs.
  • StartStreamTranscription: Real-time transcription for streaming media.

Improving Transcription Accuracy**:

  • Custom Vocabulary: Enhance transcription of unique terms (e.g., brand names, technical terms).
  • Custom-Language Model: Train with domain-specific text for context recognition.

Vocabulary Management:

  • Vocabulary Filter: Masks or filters specific words (e.g., offensive language).
  • Content Redaction: Automatic masking/removal of sensitive information (e.g., credit card numbers).

Additional Features:

  • Language Identification: Detects language of the media file.
  • Speaker Diarization: Identifies different speakers in a media file.

Specialized Use Cases:

  • Medical and call-center analytics.
  • Detailed information in AWS Documentation.

Practical Application:

  • Suitable for diverse scenarios, from everyday conversations to technical and domain-specific dialogues.

Amazon Translate

  • Service for text translation between different languages.
  • Supports 75 languages (as of course creation).

APIs for Translation:

  • TranslateText: Real-time text translation.
  • StartTextTranslationJob: Asynchronous batch translation.
  • DescribeTextTranslationJob: Checks status and results of batch translation jobs.

Customization Options:

  • Formality setting: Adjusts translation for formal or informal language (supported in French, German, Hindi, Italian, Japanese, Spanish).
  • Profanity replacement: Uses grawlix strings for profane words/phrases.
  • Custom terminology: Control translations of specific terms, unidirectional or multidirectional.
  • Non-translated text tags: Use tags like <span> or <p> with translate=no property to exclude text from translation.
  • Parallel data: Customize translation output for specific domains (e.g., life sciences, law, finance).

Practical Use:

  • Simple translations with additional control over translation quality, style, and domain-specific language.

Amazon Lex

  • Conversational interface supporting voice and text-chat.
  • Utilizes Alexa’s conversational engine.
  • Fully managed service with automatic scaling, backup, and versioning.

Conversation Example: Booking a car.

  • Key elements: Destination city and departure date.

Bot Configuration in Amazon Lex:

  • Intents: Tasks to complete via conversation (e.g., BookCar).
  • Utterances: Phrases triggering an intent.
  • Slots: Parameters required to fulfill an intent.
  • Prompts: Questions eliciting slot responses.

Intent Configuration:

  • PromptSpecification: Message confirming intent completion.
  • DeclinationResponse: Used if user refuses promptSpecification.
  • DialogCodeHook: AWS Lambda function for additional conversational logic.
  • FulfillmentCodeHook: Passes completed intent to a Lambda function for processing or local device actions.

Slot Types:

  • Defined questions for user response.
  • EnumerationValues for training or validation.
  • Two modes: Expand values (training data) or restrict to set values (validation).

Built-in Intents and Slot Types:

  • Common intents (e.g., HelpIntent, CancelIntent) and slot types (e.g., dates, numbers) pre-configured.

Integration with External Systems:

  • Customization of user interactions.
  • Integration with databases or external systems via Lambda functions.
  • DialogCodeHook: Initialization and validation.
  • FulfillmentCodeHook: Post-intent processing and direction.

Lambda Function Integration:

  • Receives context (bot, intent, slots) for processing.
  • Directs Lex for next steps using dialog actions (e.g., Close, ConfirmIntent, Delegate, ElicitIntent, ElicitSlot).

Amazon SageMaker

  • Designed for preparing, building, training, and deploying machine learning models.
  • Offers fine control over model creation and deployment.

Jupyter Notebooks in SageMaker:

  • Interactive environment for code, documentation, and visualizations.
  • Supports data preparation, visualization, training, and deploying models.
  • Can clone and run notebooks from GitHub.

Training a Model in SageMaker:

  • Variety of algorithms available: SageMaker-provided, custom, or from AWS Marketplace.
  • Custom algorithms via Docker containers.
  • Selection of training instance types (standard, compute-optimized, GPU-accelerated).
  • Hyperparameter tuning for algorithm optimization.
  • Input data channels from S3 or EFS, format depends on the algorithm.

AWS Marketplace:

  • Buy or sell trained models and algorithms.
  • Marketplace offers pre-trained models or algorithms for training.

Hosting Models for Inferences:

  • Real-Time Inference: API endpoint for real-time model access.
  • Serverless Inference: Cost-effective for infrequent or unpredictable traffic.
  • Asynchronous Endpoint: Queues requests for processing with large payloads/long processing times.
  • Batch Transformation: Process data in batches without a long-lived endpoint.

Practical Applications:

  • Real-time applications like fraud detection.
  • Batch processing for non-real-time requirements.

Further Learning in SageMaker:

  • Advanced machine learning capabilities for deeper exploration.

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