Content Domain 1: Fundamentals of AI and ML

Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language models(LLMs)).

Describe the similarities and differences between AI, ML, GenAI, and deep learning.

Describe various types of inferencing (for example, batch, real-time).

Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).

Describe supervised learning, unsupervised learning, and reinforcement learning.

Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).

Determine when AI/ML solutions are not appropriate (for example, cost-benefit analyses, situations when a specific outcome is needed instead of a prediction).

Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering).

Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting).

Explain the capabilities of AWS managed AI/ML services (for example, Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

Describe components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring).

Describe sources of ML models (for example, open source pre-trained models, training custom models).

Describe methods to use a model in production (for example, managed API service, self-hosted API).

Identify relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker AI, SageMaker Data Wrangler, SageMaker Feature Store, SageMaker Model Monitor).

Describe fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training).

Describe model performance metrics (for example, accuracy, Area Under the Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.