Understanding AWS AI and ML

Introduction to Cloud Computing & AWS

• What is Cloud Computing? (IaaS, PaaS, SaaS)
• Overview of the AWS Global Infrastructure (Regions, Availability Zones).
• Navigating the AWS Management Console.
• Setting Up Your AWS Subscription.
• Understanding AWS Free Usage Tier.
• Understanding billing and Management Portals.
• Core AWS Services: S3, EC2, IAM, etc.

Foundational AI & Machine Learning on AWS

Before diving into specific services, this module covers the essential theory behind AI and ML.

• What is AI and ML?
• Types of AI: Narrow, General, and Superintelligence.
• Key concepts: Supervised Learning (Classification, Regression), Unsupervised Learning (Clustering, Association), Reinforcement Learning, neural networks, bias, and ethics.
• Common AI/ML use cases and business applications.
• Key terminology: Machine Learning, Deep Learning, Neural Networks.
• The AWS ML Stack: AI Services, ML Services, and ML Frameworks.
• Explain the capabilities of AWS managed AI/ML services (for example, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

AWS services that provide computer vision capabilities

This module focuses on AWS services that provide computer vision capabilities without requiring deep ML expertise. 👁️

• 3.1 Amazon Rekognition
  • Image analysis: Object and scene detection, facial analysis, celebrity recognition, text in image.
  • Video analysis: Object tracking, activity detection.
• 3.2 Amazon Textract
  • Extracting text, handwriting, and data from scanned documents, PDFs, and images.
  • Understanding forms and tables.
• 3.3 Amazon Lookout for Vision
  • Automating visual inspection for industrial quality control.

AI-Powered Language Services

Explore services that understand and process natural language. 🗣️

• 4.1 Amazon Comprehend
  • Natural Language Processing (NLP) for insights and relationships in text.
  • Key phrase extraction, sentiment analysis, entity recognition.
• 4.2 Amazon Translate
  • Real-time and batch language translation.
• 4.3 Amazon Polly
  • Turning text into lifelike speech (Text-to-Speech).
• 4.4 Amazon Transcribe
  • Converting speech to text (Speech-to-Text).

AI-Powered Conversation and Data Services

This section covers conversational AI and intelligent search. 🤖

• 5.1 Amazon Lex
  • Building conversational interfaces (chatbots) using voice and text.
  • Intents, utterances, and slots.
• 5.2 Amazon Kendra
  • Intelligent enterprise search powered by machine learning.
  • Connecting data sources and using natural language queries.
• 5.3 Amazon Personalize
  • Creating real-time personalized recommendations for users.

Introduction to Amazon SageMaker

This module introduces the flagship AWS service for building, training, and deploying custom ML models. 🧠

• 6.1 What is Amazon SageMaker?
  • Overview of the fully managed ML platform.
  • Key components: SageMaker Studio, Notebooks, Autopilot.
• 6.2 Data Preparation
  • Using Amazon SageMaker Data Wrangler for data cleaning and feature engineering.
• 6.3 Building and Training Models
  • Using built-in algorithms (e.g., XGBoost, Linear Learner).
  • Introduction to SageMaker Autopilot for automated machine learning (AutoML).
• 6.4 Model Deployment and Monitoring
  • Deploying models as real-time endpoints.
  • Introduction to model monitoring concepts.

Best Practices and Looking Ahead

This final module ties everything together, focusing on real-world application and governance.

• 7.1 AWS Well-Architected Framework for ML
  • Designing reliable, secure, efficient, and cost-effective ML workloads.
• 7.2 Responsible AI on AWS
  • Concepts of fairness, explainability, and governance in AI.
  • Introduction to Amazon SageMaker Clarify.
• 7.3 Combining AWS AI Services
  • Architecting solutions that use multiple services (e.g., Transcribe + Comprehend + Kendra).
• Capstone Project: Finalizing and Presenting a Solutions
  • Students will be given a business problem and will design a solution architecture using a combination of AWS AI services. They will present their solution, explaining their service choices and the data flow.