AWS Certified AI Practitioner (AIF-C01)
ISBN: 9780135420782 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 43m | 1.49 GB
Instructor: Chad Smith
ISBN: 9780135420782 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 43m | 1.49 GB
Instructor: Chad Smith
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Introduction
AWS Certified AI Practitioner (AIF-C01): Introduction
Module 1: Exam Foundation
Module Introduction
Lesson 1: Exam Guide
Learning objectives
1.1 Introduction
1.2 Target Candidate Description
1.3 Exam Content
1.4 Exam Question Domains
Module 2: Fundamentals of AI and ML
Module Introduction
Lesson 2: Basic AI Concepts
Learning objectives
2.1 Basic AI Terminology
2.2 Introduction to Machine Learning
2.3 Introduction to Deep Learning
2.4 Question Breakdown 1
2.5 Question Breakdown 2
Lesson 3: Practical Use Cases For AI
Learning objectives
3.1 AI Patterns and Anti-patterns
3.2 ML Techniques
3.3 Real-world AI Applications
3.4 AWS Managed AI/ML Services
3.5 Question Breakdown 1
3.6 Question Breakdown 2
Lesson 4: ML Development Lifecycle
Learning objectives
4.1 ML Pipeline Components
4.2 ML Model Sources and Deployment Types
4.3 Introduction to ML Ops
4.4 AWS ML Pipeline Services
4.5 ML Model Performance Metrics
4.6 Question Breakdown 1
4.7 Question Breakdown 2
Module 3: Fundamentals of 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: AWS Generative AI Offerings
Learning objectives
7.1 AWS Generative AI Services and Features
7.2 AWS Generative AI Advantages and Benefits
7.3 AWS Generative AI Cost Tradeoffs
7.4 Question Breakdown 1
7.5 Question Breakdown 2
Module 4: Applications of Foundation Models
Module Introduction
Lesson 8: Foundation Model Design
Learning objectives
8.1 Pre-trained Model Selection Criteria
8.2 Model Inference Parameters
8.3 Introduction to RAG
8.4 Introduction to Vector Databases
8.5 AWS Vector Database Service
8.6 Foundation Model Customization Cost Tradeoffs
8.7 Generative AI Agents
8.8 Question Breakdown 1
8.9 Question Breakdown 2
Lesson 9: Foundation Model Performance
Learning objectives
9.1 Foundation Model Performance Metrics and Evaluation
9.2 Foundation Model Business Objective Criteria
9.3 Question Breakdown 1
9.4 Question Breakdown 2
Lesson 10: Foundation Model Training and Fine Tuning
Learning objectives
10.1 Foundation Model Training
10.2 Foundation Model Fine-tuning
10.3 Foundation Model Data Preparation
10.4 Question Breakdown 1
10.5 Question Breakdown 2
Lesson 11: Prompt Engineering
Learning objectives
11.1 Prompt Workflow
11.2 Prompt Engineering Concepts
11.3 Prompt Engineering Techniques
11.4 Prompt Engineering Best Practices
11.5 Prompt Engineering Risks and Limitations
11.6 Question Breakdown 1
11.7 Question Breakdown 2
Module 5: Responsible and Secure AI Solutions
Module Introduction
Lesson 12: Responsible AI System Development
Learning objectives
12.1 Responsible AI Features
12.2 AWS Responsible AI Tools
12.3 Responsible AI Model Selection Practices
12.4 Generative AI Legal Risks
12.5 AI Dataset Characteristics
12.6 AI Bias and Variance
12.7 AWS AI Bias Detection Tools
12.8 Question Breakdown 1
12.9 Question Breakdown 2
Lesson 13: Transparent and Explainable AI Models
Learning objectives
13.1 Transparency and Explainability Definitions
13.2 AWS Transparency and Explainability Tools
13.3 AI Model Safety and Transparency Tradeoffs
13.4 Human-centered AI Design Principles
13.5 Question Breakdown 1
13.6 Question Breakdown 2
Lesson 14: AI Security
Learning objectives
14.1 AWS AI Security Services and Features
14.2 Data Citations and Origin Documentation
14.3 Secure Data Engineering Best Practices
14.4 AI Security and Privacy Considerations
14.5 Question Breakdown 1
14.6 Question Breakdown 2
Lesson 15: AI Governance and Compliance
Learning objectives
15.1 AWS Governance and Compliance Services
15.2 Data Governance Strategies
15.3 Governance Protocols and Compliance Standards
15.4 Question Breakdown 1
15.5 Question Breakdown 2
Summary
AWS Certified AI Practitioner (AIF-C01): Summary
AWS Certified AI Practitioner (AIF-C01): Introduction
Module 1: Exam Foundation
Module Introduction
Lesson 1: Exam Guide
Learning objectives
1.1 Introduction
1.2 Target Candidate Description
1.3 Exam Content
1.4 Exam Question Domains
Module 2: Fundamentals of AI and ML
Module Introduction
Lesson 2: Basic AI Concepts
Learning objectives
2.1 Basic AI Terminology
2.2 Introduction to Machine Learning
2.3 Introduction to Deep Learning
2.4 Question Breakdown 1
2.5 Question Breakdown 2
Lesson 3: Practical Use Cases For AI
Learning objectives
3.1 AI Patterns and Anti-patterns
3.2 ML Techniques
3.3 Real-world AI Applications
3.4 AWS Managed AI/ML Services
3.5 Question Breakdown 1
3.6 Question Breakdown 2
Lesson 4: ML Development Lifecycle
Learning objectives
4.1 ML Pipeline Components
4.2 ML Model Sources and Deployment Types
4.3 Introduction to ML Ops
4.4 AWS ML Pipeline Services
4.5 ML Model Performance Metrics
4.6 Question Breakdown 1
4.7 Question Breakdown 2
Module 3: Fundamentals of 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: AWS Generative AI Offerings
Learning objectives
7.1 AWS Generative AI Services and Features
7.2 AWS Generative AI Advantages and Benefits
7.3 AWS Generative AI Cost Tradeoffs
7.4 Question Breakdown 1
7.5 Question Breakdown 2
Module 4: Applications of Foundation Models
Module Introduction
Lesson 8: Foundation Model Design
Learning objectives
8.1 Pre-trained Model Selection Criteria
8.2 Model Inference Parameters
8.3 Introduction to RAG
8.4 Introduction to Vector Databases
8.5 AWS Vector Database Service
8.6 Foundation Model Customization Cost Tradeoffs
8.7 Generative AI Agents
8.8 Question Breakdown 1
8.9 Question Breakdown 2
Lesson 9: Foundation Model Performance
Learning objectives
9.1 Foundation Model Performance Metrics and Evaluation
9.2 Foundation Model Business Objective Criteria
9.3 Question Breakdown 1
9.4 Question Breakdown 2
Lesson 10: Foundation Model Training and Fine Tuning
Learning objectives
10.1 Foundation Model Training
10.2 Foundation Model Fine-tuning
10.3 Foundation Model Data Preparation
10.4 Question Breakdown 1
10.5 Question Breakdown 2
Lesson 11: Prompt Engineering
Learning objectives
11.1 Prompt Workflow
11.2 Prompt Engineering Concepts
11.3 Prompt Engineering Techniques
11.4 Prompt Engineering Best Practices
11.5 Prompt Engineering Risks and Limitations
11.6 Question Breakdown 1
11.7 Question Breakdown 2
Module 5: Responsible and Secure AI Solutions
Module Introduction
Lesson 12: Responsible AI System Development
Learning objectives
12.1 Responsible AI Features
12.2 AWS Responsible AI Tools
12.3 Responsible AI Model Selection Practices
12.4 Generative AI Legal Risks
12.5 AI Dataset Characteristics
12.6 AI Bias and Variance
12.7 AWS AI Bias Detection Tools
12.8 Question Breakdown 1
12.9 Question Breakdown 2
Lesson 13: Transparent and Explainable AI Models
Learning objectives
13.1 Transparency and Explainability Definitions
13.2 AWS Transparency and Explainability Tools
13.3 AI Model Safety and Transparency Tradeoffs
13.4 Human-centered AI Design Principles
13.5 Question Breakdown 1
13.6 Question Breakdown 2
Lesson 14: AI Security
Learning objectives
14.1 AWS AI Security Services and Features
14.2 Data Citations and Origin Documentation
14.3 Secure Data Engineering Best Practices
14.4 AI Security and Privacy Considerations
14.5 Question Breakdown 1
14.6 Question Breakdown 2
Lesson 15: AI Governance and Compliance
Learning objectives
15.1 AWS Governance and Compliance Services
15.2 Data Governance Strategies
15.3 Governance Protocols and Compliance Standards
15.4 Question Breakdown 1
15.5 Question Breakdown 2
Summary
AWS Certified AI Practitioner (AIF-C01): Summary