1.3.2. SageMaker Components and When to Use Them
💡 First Principle: Each SageMaker component solves one specific problem in the ML lifecycle. The exam tests your ability to match the problem description to the correct component—not your knowledge of SageMaker's marketing materials.
| Component | Lifecycle Stage | Primary Use | Key Exam Signals |
|---|---|---|---|
| Data Wrangler | Data Prep | Visual data prep, feature engineering | "Minimal code," "visual interface," "data exploration" |
| Feature Store | Data Prep | Centralized feature repository | "Reuse features," "online/offline store," "feature consistency" |
| Ground Truth | Data Prep | Data labeling workflows | "Labeling," "annotation," "human reviewers" |
| Training Jobs | Model Dev | Scalable model training | "Train at scale," "distributed training," "GPU instances" |
| Automatic Model Tuning (AMT) | Model Dev | Hyperparameter optimization | "Hyperparameter tuning," "Bayesian optimization," "best parameters" |
| JumpStart | Model Dev | Pre-trained models and solutions | "Pre-trained," "transfer learning," "quick start" |
| Experiments | Model Dev | Track and compare training runs | "Compare experiments," "track metrics," "reproducibility" |
| Model Registry | Model Dev → Deploy | Model versioning and approval | "Model version," "approval workflow," "model governance" |
| Clarify | Model Dev + Monitor | Bias detection and explainability | "Bias," "explainability," "SHAP values," "fairness" |
| Debugger | Model Dev | Debug training convergence issues | "Vanishing gradients," "exploding gradients," "overfitting during training" |
| Endpoints | Deployment | Real-time inference serving | "Real-time predictions," "low latency," "endpoint" |
| Batch Transform | Deployment | Large-scale offline inference | "Batch predictions," "no persistent endpoint," "offline scoring" |
| Pipelines | Deployment | ML workflow orchestration | "Automate workflow," "DAG," "orchestrate steps" |
| Neo | Deployment | Model optimization for edge | "Edge deployment," "optimize model," "IoT" |
| Model Monitor | Monitoring | Data/model drift detection | "Drift," "data quality," "production monitoring," "baseline" |
| Inference Recommender | Monitoring | Endpoint sizing recommendations | "Rightsizing," "instance selection," "load testing" |
⚠️ Exam Trap: SageMaker Clarify appears in two lifecycle stages: pre-training bias detection (Stage 2) and post-deployment monitoring (Stage 4). Similarly, Feature Store has an online store (low-latency inference lookups) and an offline store (batch training data). Questions test whether you understand these dual roles.
Reflection Question: A team notices their model's predictions disproportionately affect one demographic group. Which SageMaker component should they use, and does it matter whether the model is in development or already deployed?
