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Module 8: AI/ML and Data Analytics

Module 8: AI/ML and Data Analytics

AWS AI can be used to analyze data, automate processes, and enhance decision-making across various industries. Key services include:

  • Amazon SageMaker
  • AWS Deep Learning AMIs
  • AWS DeepLens
  • Amazon Rekognition
  • Amazon Comprehend
  • Amazon Lex
  • Amazon Polly

These services enable developers to build, train, and deploy machine learning models quickly and efficiently.

AI is the development of intelligent computer systems capable of performing human-like tasks.

Machine learning is a subset of AI that trains machines to perform complex tasks. It works by finding patterns in data and making predictions based on those patterns.

AI and Machine Learning Services on AWS

Used to run product recommendation engines, fraud detection, and predictive analytics.

Amazon Polly for text-to-speech capabilities.

Amazon Rekognition for image and video analysis.

Amazon Comprehend for natural language processing.

Amazon Lex for building conversational interfaces.

AWS DeepLens for computer vision applications.

Amazon SageMaker for building, training, and deploying machine learning models.

Tier 1 — Pre-built AWS AI Services

These are pre-built AI models, each designed to perform a specific function:

  • Language services
    • Amazon Comprehend - extract insights from documents
    • Amazon Polly - Text to speech
    • Amazon Transcribe - Speech to text
    • Amazon Translate - Language translation
  • Computer vision
    • Amazon Kendra - Intelligent search service
    • Amazon Rekognition - Image and video analysis
    • Amazon Textract - Document text extraction
  • Conversation AI and personalization
    • Amazon Lex - Build conversational interfaces
    • Amazon Personalize - Real-time personalization and recommendation

Tier 2 — ML Services

  • Amazon SageMaker - Build, train, and deploy machine learning models
  • AWS Deep Learning AMIs - Pre-built environments for deep learning
  • AWS DeepLens - Deep learning-enabled video camera
  • Amazon Forecast - Time series forecasting
  • Amazon Fraud Detector - Identify fraudulent activity
  • Amazon Lookout for Metrics - Anomaly detection for metrics
  • Amazon Kinesis - Real-time data streaming and analytics

Tier 3 — ML Frameworks and Infrastructure

  • An ML framework is a software library designed to help developers build and train machine learning models.
  • Common examples include TensorFlow, PyTorch, and Apache MXNet.
  • AWS ML infrastructure includes services such as Amazon EC2, Amazon S3, and AWS Lambda to support the compute, storage, and event-driven needs of machine learning workloads.

Generative AI on AWS

Deep learning is a subset of machine learning where models are trained using layers of artificial neural networks. These models can generate new content, such as images, text, and music, by learning from existing data.

Generative AI is a type of deep learning powered by large ML models. These can be adapted to perform multiple tasks.

  • Amazon SageMaker JumpStart — A collection of pre-built models and solutions to help you get started with generative AI quickly.
  • Amazon Bedrock — A fully managed service that makes it straightforward to build and scale generative AI applications using foundation models from multiple providers.
  • Amazon Q — An interactive AI assistant that can integrate with your organisation's information repositories to answer questions and assist with tasks.

Introduction to Data Analytics on AWS

ETL (Extract, Transform, Load) data pipelines ingest data into a centralised location for analysis and reporting. AWS offers a range of services to support data analytics:

  • Amazon Kinesis for real-time data streaming and analytics
  • Amazon Data Firehose for loading streaming data into data lakes, warehouses, and analytics services
  • AWS Glue for data preparation/processing and ETL
  • Amazon Redshift for data warehousing
  • Amazon QuickSight for business intelligence and data visualization
  • Amazon EMR for big data processing and analytics
  • Amazon OpenSearch Service for search and analytics
  • Amazon Athena for interactive query service using SQL queries

These services enable organisations to gain insights from their data and make data-driven decisions.

Data pipelines automate the process of collecting, processing, and analysing data, allowing organisations to focus on deriving insights and making informed decisions.

Amazon Data Firehose can invoke a Lambda function to transform the data before loading it into the destination.