1. Foundations of AI and AWS (1.5 hours)

1.1 AI Fundamentals

  • Understanding Artificial Intelligence: This section introduces participants to the core concepts of AI, providing a clear definition and understanding of what AI is and how it differs from traditional software. It explores the various subfields of AI, including machine learning, deep learning, and natural language processing (NLP).
  • Impact of AI on Modern Technology: Participants will learn how AI is transforming industries, from healthcare to finance, and its role in driving innovation. Real-world examples will be used to illustrate the profound impact AI has on modern technology, business processes, and society at large.

1.2 AWS Cloud Essentials

  • Cloud Computing Basics: This module provides a foundational understanding of cloud computing, explaining the benefits, models (IaaS, PaaS, SaaS), and deployment strategies (public, private, hybrid clouds). It sets the stage for understanding AWS’s role in the cloud ecosystem.
  • AWS Account Setup and Management: Participants will receive step-by-step guidance on creating and managing an AWS account. This includes setting up user permissions, managing resources, and configuring billing preferences to ensure a secure and cost-effective environment.
  • Cost Optimization with AWS Budgets: This part of the course covers strategies for managing and optimizing cloud costs. It introduces AWS Budgets, a tool that helps users monitor their AWS usage and costs, providing alerts when usage exceeds defined thresholds.

2. Machine Learning Fundamentals (2 hours)

2.1 Core ML Concepts

  • Differentiating AI, ML, and Deep Learning: This section demystifies the differences between AI, machine learning, and deep learning, explaining how they relate to each other and their respective roles in the AI ecosystem.
  • Generative AI Introduction: Participants will be introduced to generative AI, a subset of AI that involves creating new data, such as images, text, or music, from learned patterns. This section sets the stage for more advanced topics later in the course.

2.2 ML Techniques

  • Supervised Learning: This module covers the fundamentals of supervised learning, where a model is trained on labeled data. Participants will learn about common algorithms, such as linear regression and decision trees, and their applications.
  • Unsupervised Learning: In contrast to supervised learning, this section focuses on unsupervised learning techniques, where the model identifies patterns in unlabeled data. Key algorithms like clustering and association will be discussed.
  • Reinforcement Learning: This advanced ML technique involves training models to make a sequence of decisions by rewarding desired outcomes. Participants will explore its applications, including gaming and robotics.

2.3 ML Project Lifecycle

  • Data Preparation and Model Training: This module walks participants through the end-to-end process of preparing data for machine learning, training a model, and evaluating its performance. It emphasizes the importance of data quality and feature engineering.
  • Evaluation Metrics and Best Practices: Participants will learn about key metrics used to evaluate ML models, such as accuracy, precision, recall, and F1-score. Best practices for selecting and tuning models to achieve optimal performance will also be covered.

3. Amazon Bedrock and Generative AI (3 hours)

3.1 Generative AI Deep Dive

  • Understanding Generative AI Capabilities: This section delves into the mechanisms behind generative AI models, including neural networks like GANs (Generative Adversarial Networks) and transformers. Participants will explore how these models generate realistic outputs from minimal input.
  • Real-World Applications and Use Cases: Practical examples of generative AI will be discussed, including content creation, art, and simulation. Participants will examine how companies leverage generative AI to innovate and gain competitive advantages.

3.2 Amazon Bedrock Mastery

  • Foundation Models Exploration: This module introduces Amazon Bedrock, a suite of foundation models pre-trained on vast datasets. Participants will explore how these models can be customized and applied to a variety of tasks, such as text generation, summarization, and translation.
  • Hands-on Bedrock Implementation: Participants will engage in hands-on labs to implement Amazon Bedrock in real-world scenarios, gaining practical experience in deploying and fine-tuning these models for specific business needs.
  • RAG and Knowledge Base Utilization: This section covers the use of Retrieval-Augmented Generation (RAG) with Bedrock, enabling participants to enhance AI capabilities with a knowledge base. Practical examples will illustrate how to integrate external data sources to improve AI performance.
  • Agents and GuardRails Implementation: Participants will learn how to implement agents and guardrails in AI applications, ensuring that generative AI outputs are safe, reliable, and aligned with business objectives.

4. Advanced AI Techniques (2 hours)

4.1 Prompt Engineering Mastery

  • Crafting Effective Prompts: This module teaches the art of prompt engineering, a critical skill for guiding AI models to generate desired outcomes. Participants will learn how to craft and optimize prompts for various AI applications.
  • Optimization Strategies for Better Results: Advanced techniques for refining prompts and iterating on model responses will be explored, helping participants achieve more accurate and contextually appropriate outputs.

4.2 Amazon Q Exploration

  • Leveraging Amazon Q for Business: Amazon Q is introduced as a tool for querying and analyzing large datasets using natural language. Participants will explore how Amazon Q can be integrated into business workflows to drive data-driven decisions.
  • Developer Tools and Applications: This section covers the various developer tools available within Amazon Q, enabling participants to build custom applications and leverage AI-driven insights across their organizations.

5. AWS AI Services Ecosystem (3 hours)

5.1 Language and Text Services

  • Amazon Comprehend for NLP: Participants will learn how to use Amazon Comprehend to extract insights from text, including sentiment analysis, entity recognition, and key phrase extraction.
  • Amazon Translate for Multilingual Applications: This section explores Amazon Translate, a service that enables applications to support multiple languages, breaking down language barriers in global communication.

5.2 Vision and Speech Services

  • Amazon Rekognition for Image and Video Analysis: Participants will dive into Amazon Rekognition, learning how to apply AI for image and video analysis, including object detection, facial recognition, and content moderation.
  • Amazon Polly for Text-to-Speech Solutions: This module introduces Amazon Polly, a service that converts text into natural-sounding speech, enabling the creation of voice-enabled applications.

5.3 Conversational AI and Personalization

  • Building Chatbots with Amazon Lex: Participants will explore Amazon Lex, a service for building conversational interfaces. They will learn how to design, implement, and deploy chatbots that can engage users in natural language conversations.
  • Personalizing User Experiences with Amazon Personalize: This section covers Amazon Personalize, a service that enables the creation of personalized recommendations for users, enhancing customer engagement and satisfaction.

5.4 Enterprise Search and Document Analysis

  • Intelligent Search with Amazon Kendra: Participants will learn how to use Amazon Kendra to build intelligent search solutions that deliver accurate and relevant information from vast datasets.
  • Document Processing with Amazon Textract: This module covers Amazon Textract, a service that automatically extracts text, forms, and tables from scanned documents, streamlining document processing workflows.

6. Amazon SageMaker Essentials (2 hours)

6.1 SageMaker Overview

  • Understanding SageMaker’s Capabilities: This section introduces Amazon SageMaker, a comprehensive machine learning platform that enables developers to build, train, and deploy models at scale. Participants will explore the platform’s core features and benefits.
  • SageMaker Studio Environment: Participants will get hands-on experience with SageMaker Studio, an integrated development environment (IDE) for machine learning, learning how to navigate and utilize its tools effectively.

6.2 End-to-End ML with SageMaker

  • Data Preparation Techniques: This module focuses on best practices for preparing data in SageMaker, including data wrangling, cleaning, and transformation techniques.
  • Model Training and Deployment: Participants will learn how to train ML models in SageMaker and deploy them to production, ensuring scalability and reliability in their AI solutions.
  • Hands-on SageMaker Project: This section culminates in a hands-on project, where participants apply what they’ve learned to build and deploy a machine learning model from start to finish using SageMaker.

7. Responsible AI and Security (2 hours)

7.1 Ethical AI Practices

  • AI Ethics and Governance: This module covers the ethical considerations of AI development and deployment, including bias mitigation, transparency, and fairness. Participants will learn about frameworks and guidelines for ethical AI governance.
  • Compliance in AI Applications: Participants will explore the regulatory landscape surrounding AI, including GDPR, HIPAA, and other compliance requirements, ensuring that AI applications adhere to legal standards.

7.2 MLOps Introduction

  • Streamlining ML Workflows: MLOps, or Machine Learning Operations, is introduced as a discipline that combines machine learning, DevOps, and data engineering. Participants will learn how to streamline ML workflows for more efficient and reliable model deployment.
  • Best Practices for ML Operations: This section covers best practices for managing the ML lifecycle, from versioning and monitoring to continuous integration and continuous deployment (CI/CD) in AI applications.

7.3 Securing AI Applications on AWS

  • IAM Fundamentals for AI: Participants will learn about Identity and Access Management (IAM) in AWS, focusing on securing AI applications by managing user access and permissions.
  • Key Security Services: EC2, Lambda, CloudTrail: This module covers essential AWS security services, including EC2 (Elastic Compute Cloud) for compute security, Lambda for serverless application security, and CloudTrail for auditing and monitoring AI application activities.

8. Real-World AI Applications (3 hours)

8.1 Conversational AI Implementation

  • Building an Intelligent Chatbot: This section walks participants through the process of designing and deploying a conversational AI solution using AWS services, with a focus on real-world use cases like customer support bots.

8.2 Computer Vision Projects

  • Image Analysis and Object Detection: Participants will explore computer vision applications, including image analysis and object detection, using AWS services like Rekognition and custom ML models.

8.3 Natural Language Processing Applications

  • Text Analysis and Sentiment Detection: This module covers advanced NLP applications, including sentiment analysis, text classification, and entity recognition, using AWS AI services and custom models.

8.4 Fraud Detection Systems

  • Introduction to AI-Powered Fraud Prevention: Participants will learn how AI can be applied to detect and prevent fraud, exploring use cases in finance, e-commerce, and cybersecurity. Techniques like anomaly detection and pattern recognition will be discussed.

9. AWS Certified AI Practitioner Exam Prep (1.5 hours)

9.1 Comprehensive Review

  • Key Concepts and Services Recap: This section provides a thorough review of all the key concepts, AWS services, and best practices covered in the course, ensuring participants are well-prepared for the certification exam.

9.2 Exam Strategies

  • Practice Questions and Scenarios: Participants will engage in practice exams and scenario-based questions to familiarize themselves with the exam format and types of questions they will encounter.
  • Time Management and Test-Taking Tips: This final module offers strategies for managing time effectively during the exam, as well as tips for approaching complex questions and scenarios to maximize the chances of success.

This detailed course outline provides a comprehensive roadmap for mastering AI on AWS, equipping participants with the knowledge and skills necessary to achieve AWS Certified AI Practitioner certification and apply AI solutions effectively in real-world scenarios.