1.1.2 - Describe the similarities and differences between AI, ML, and deep learning

1.1.3 - Describe various types of inferencing

1.1.4 - Describe the different types of data in AI models

1.1.5 - Describe supervised learning, unsupervised learning, and reinforcement learning

1.2.1 - Recognize applications where AI/ML can provide value

1.2.2 - Determine when AI/ML solutions are not appropriate

1.2.3 - Select the appropriate ML techniques for specific use cases

1.2.4 - Identify examples of real-world AI applications

1.2.5 - Explain the capabilities of Aws managed AI/ML services

1.3.1 - Describe components of an ML pipeline

1.3.2 - Understand sources of ML models

1.3.3 - Describe methods to use a model in production

1.3.4 - Identify relevant Aws services and features for each stage of an ML pipeline

1.3.5 - Understand fundamental concepts of ML operations

1.3.6 - Understand model performance metrics and business metrics to evaluate ML models

2.1.1 - Understand foundational generative AI concepts

2.1.2 - Identify potential use cases for generative AI models

2.1.3 - Describe the foundation model lifecycle

2.2.1 - Describe the advantages of generative AI

2.2.2 - Identify disadvantages of generative AI solutions

2.2.3 - Understand various factors to select appropriate generative AI models

2.2.4 - Determine business value and metrics for generative AI applications

2.3.1 - Identify Aws services and features to develop generative AI applications

2.3.2 - Describe the advantages of using Aws generative AI services to build applications

2.3.3 - Understand the benefits of Aws infrastructure for generative AI applications

2.3.4 - Understand cost tradeoffs of Aws generative AI services