AWS MLA-C01 glossary of ML pipeline, drift, endpoint, deployment, and monitoring terms.
Use this glossary when SageMaker and MLOps terms start to blur together. Keep it beside the cheat sheet and resources rather than treating it as a substitute for study.
| Term | Short meaning | Why it matters on MLA-C01 |
|---|---|---|
| MLOps | Deployment, monitoring, versioning, and lifecycle discipline for ML systems | The exam is more engineering and operations than pure modeling theory |
| Feature store | Managed store for reusable model features | Prevents train/serve skew and supports repeated feature use |
| Endpoint | Hosted inference interface for serving model predictions | Central to real-time, async, and multi-model serving choices |
| Batch transform | Offline inference over a dataset rather than real-time requests | Tested against real-time and async serving patterns |
| Model registry | Managed inventory of model versions and approval states | Critical for rollback, traceability, and safe promotion |
| Drift | Production data or behavior changing away from the training or expected pattern | Core monitoring concept in live ML systems |
| Clarify | SageMaker tool for explainability and bias-related analysis | Often appears in fairness and explainability questions |
| Model Monitor | SageMaker monitoring capability for production models | Strongest first answer for drift and data-quality monitoring |
| Pipeline | Orchestrated ML workflow such as prepare, train, validate, and deploy | Key to retraining, CI/CD, and repeatability |
| Shadow deployment | Comparing production traffic against a new model without full cutover | Safer comparison than blind promotion |
| Blue/green deployment | Safer rollout pattern with a separate replacement environment | Helps reduce blast radius during rollout |
| Inference recommender | SageMaker guidance for deployment instance and configuration fit | Helps connect model serving choices to cost and capacity |
| Data Wrangler | Managed data-prep workflow for transformations and feature work | Strong answer in data preparation questions |
| Hyperparameter tuning | Systematic search across training settings | Distinct from model choice and deployment tuning |
| Multi-model endpoint | Shared endpoint that serves several low-traffic models | Cost and serving-fit concept, not a training concept |
| VPC isolation | Keeping inference resources inside private network boundaries | Common security and deployment control on MLA-C01 |
| Train/serve skew | Difference between how features are built for training versus live inference | A classic feature-store and data-pipeline problem |
| Baseline | Reference dataset or statistics used to compare later inference behavior | Central to drift and monitoring questions |
| Ground truth | Actual observed outcome used later to assess prediction quality | Needed to reason about delayed quality evaluation |
| Lineage | Record of how data, code, training, and models relate across versions | Helps with auditability, rollback, and governance |
| Serverless inference | Managed inference that scales down when idle | Often the right answer for spiky low-duty-cycle traffic |
| Async inference | Inference pattern where the request returns later rather than immediately | Better fit for long-running or bursty jobs than always-on real-time serving |
| Pair | Keep this distinction clear |
|---|---|
| online inference vs batch transform | low-latency serving versus offline dataset scoring |
| drift vs bias | changing production behavior versus unfair or skewed model behavior |
| registry vs endpoint | managed version catalog versus live serving target |
| monitoring vs rollback | detecting trouble versus returning to a safer version |
| feature store vs raw training data | reusable engineered features versus general source data |
| batch vs async inference | scheduled or offline scoring versus delayed-response online serving |
| model quality vs infra health | whether the predictions are still good versus whether the platform is still healthy |
| Clarify vs Model Monitor | fairness and explainability analysis versus production drift/data monitoring |
| feature engineering vs labeling | improving input signal versus creating target values |
| train/serve skew vs drift | inconsistent feature logic versus production behavior changing over time |
| lineage vs registry | end-to-end record of artifacts and steps versus governed model version catalog |
| Cluster | Fast separation |
|---|---|
| endpoint / registry / pipeline | endpoint serves, registry tracks versions, pipeline orchestrates workflow |
| drift / data quality / infra issue | drift means behavior changed over time, data quality means input is malformed or incomplete, infra issue means the platform is slow or unstable |
| Data Wrangler / Feature Store / Model Monitor | Data Wrangler prepares data, Feature Store serves reusable features, Model Monitor watches live inference data |
| real-time / async / batch | real-time answers now, async answers later, batch scores offline datasets |
| IAM / VPC isolation / encryption | IAM controls who, VPC controls where from, encryption protects the data itself |
| registry / lineage / approval | registry stores model versions, lineage shows how they were produced, approval decides whether they move forward |
| Cluster | What it usually signals on the exam |
|---|---|
| data quality / leakage / train-serve skew | fix the pipeline and feature logic before changing the model |
| tuning / evaluation / approval | improve the model and decide whether it is promotable |
| endpoint fit / autoscaling / inference recommender | match serving shape and cost to traffic reality |
| drift / baseline / ground truth | decide whether the model is still behaving acceptably in production |
| IAM / VPC / KMS / secrets | separate identity, network boundary, encryption, and secret handling |
| Topic family | Best page to revisit |
|---|---|
| deployment and MLOps quick rules | Cheat Sheet |
| official AWS facts and service docs | Resources |
| pacing and review order | Study Plan |
| overall exam framing | Guide root |
| training, tuning, and model versioning | 2.2 Training, Tuning & Versions |
| drift, A/B testing, and live monitoring | 4.1 Monitoring, Drift & A/B |
| endpoint shapes and scaling | 3.1 Endpoints & Containers |