Ai Concepts For Tech Professionals
 

Introduction
AI Concepts for Tech Professionals: Introduction
Module 1: Introduction to Artificial Intelligence
Module Introduction
Lesson 1: Basic AI Concepts
Learning objectives
1.1 Basic AI Terminology
1.2 Introduction to Machine Learning
1.3 Introduction to Deep Learning
1.4 Question Breakdown 1
1.5 Question Breakdown 2
Lesson 2: Practical Use Cases For AI
Learning objectives
2.1 AI Patterns and Anti-patterns
2.2 ML Techniques
2.3 Real-world AI Applications
2.4 AWS Managed AI/ML Services
2.5 Question Breakdown 1
2.6 Question Breakdown 2
Module 2: Foundation Models
Module Introduction
Lesson 3: Foundation Model Design
Learning objectives
3.1 Pre-trained Model Selection Criteria
3.2 Model Inference Parameters
3.3 Introduction to RAG
3.4 Introduction to Vector Databases
3.5 AWS Vector Database Service
3.6 Foundation Model Customization Cost Tradeoffs
3.7 Generative AI Agents
3.8 Question Breakdown 1
3.9 Question Breakdown 2
Lesson 4: Foundation Model Training, Performance, and Fine Tuning
Learning objectives
4.1 Foundation Model Training
4.2 Foundation Model Performance Metrics and Evaluation
4.3 Foundation Model Business Objective Criteria
4.4 Foundation Model Fine-tuning
4.5 Foundation Model Data Preparation
4.6 Question Breakdown 1
4.7 Question Breakdown 2
Module 3: Generative AI
Module Introduction
Lesson 5: Basic Concepts of Generative AI
Learning objectives
5.1 Basic Generative AI Terminology
5.2 Generative AI Use Cases
5.3 Foundation Model Lifecycle
5.4 Question Breakdown 1
5.5 Question Breakdown 2
Lesson 6: Generative AI Capabilities and Limitations
Learning objectives
6.1 Generative AI Advantages
6.2 Generative AI Disadvantages
6.3 Model Selection Decision Tree
6.4 Generative AI Business Value and Metrics
6.5 Question Breakdown 1
6.6 Question Breakdown 2
Lesson 7: Prompt Engineering
Learning objectives
7.1 Prompt Workflow
7.2 Prompt Engineering Concepts
7.3 Prompt Engineering Techniques
7.4 Prompt Engineering Best Practices
7.5 Prompt Engineering Risks and Limitations
7.6 Question Breakdown 1
7.7 Question Breakdown 2
Module 4: AI/ML Workload Development
Module Introduction
Lesson 8: ML Development Lifecycle
Learning objectives
8.1 ML Pipeline Components
8.2 ML Model Sources and Deployment Types
8.3 Introduction to ML Ops
8.4 AWS ML Pipeline Services
8.5 ML Model Performance Metrics
8.6 Question Breakdown 1
8.7 Question Breakdown 2
Lesson 9: Responsible AI System Development
Learning objectives
9.1 Responsible AI Features
9.2 AWS Responsible AI Tools
9.3 Responsible AI Model Selection Practices
9.4 Generative AI Legal Risks
9.5 AI Dataset Characteristics
9.6 AI Bias and Variance
9.7 AWS AI Bias Detection Tools
9.8 Question Breakdown 1
9.9 Question Breakdown 2
Module 5: AI Safety and Security
Module Introduction
Lesson 10: Transparent and Explainable AI Models
Learning objectives
10.1 Transparency and Explainability Definitions
10.2 AWS Transparency and Explainability Tools
10.3 AI Model Safety and Transparency Tradeoffs
10.4 Human-centered AI Design Principles
10.5 Question Breakdown 1
10.6 Question Breakdown 2
Lesson 11: AI Security
Learning objectives
11.1 AWS AI Security Services and Features
11.2 Data Citations and Origin Documentation
11.3 Secure Data Engineering Best Practices
11.4 AI Security and Privacy Considerations
11.5 Question Breakdown 1
11.6 Question Breakdown 2
Lesson 12: AI Governance and Compliance
Learning objectives
12.1 AWS Governance and Compliance Services
12.2 Data Governance Strategies
12.3 Governance Protocols and Compliance Standards
12.4 Question Breakdown 1
12.5 Question Breakdown 2
Summary
AI Concepts for Tech Professionals: Summary
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