Fundamentals of AI and ML
Core vocabulary
AI is the broad goal of machines performing tasks that normally need human intelligence; machine learning (ML) is the technique of fitting models to data instead of hand-coding rules; deep learning is ML using multi-layer neural networks. Distinguish supervised learning (labelled data, e.g. classification and regression), unsupervised learning (unlabelled data, e.g. clustering and dimensionality reduction), and reinforcement learning (an agent learns from reward signals through trial and error). Know that training, validation, and test splits exist to detect overfitting (good on training data, poor on new data) versus underfitting (too simple to capture the pattern).
AWS service map
Amazon SageMaker covers the ML lifecycle: data preparation, training, tuning, deployment, and monitoring. SageMaker Ground Truth handles data labelling; SageMaker Data Wrangler handles feature preparation; SageMaker Model Monitor detects drift. Amazon Rekognition (images/video), Comprehend (text/NLP), Transcribe (speech to text), Polly (text to speech), Translate, and Forecast are pre-trained AI services that need no model training. Amazon Bedrock is the managed route to foundation models.
Traps to expect
Questions blur the line between AI, ML, and deep learning, or between a pre-trained AI service and a service that requires you to train a model. Another trap is confusing overfitting with a data-quality problem, or assuming more data always fixes a model. Watch for "fully managed, no ML expertise required" phrasing, which points to an AI service rather than SageMaker.
Rule of thumb
Ask: does the task need a custom model trained on my own labelled data? If yes, choose SageMaker. If a standard capability (vision, speech, translation, forecasting) already solves it, choose the pre-built AI service.
Worked example
A team wants to tag support emails by sentiment with no ML staff. Sentiment is a standard NLP capability, so Amazon Comprehend is the answer, not SageMaker training.