Amazon SageMaker Cheat Sheet

Updated 2026-05-21

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Amazon SageMaker is AWS's fully managed end-to-end machine learning platform, covering every stage from data preparation and feature engineering through model training, tuning, deployment, and production monitoring. It eliminates the heavy lifting of infrastructure provisioning so practitioners can focus on model quality. The platform spans a wide spectrum — from no-code tools like Canvas and Autopilot, to low-level Python SDK primitives like Estimator and HyperparameterTuner — making it relevant whether you're a data scientist, ML engineer, or business analyst. The critical mental model is that SageMaker is not a single service but a suite of tightly integrated capabilities: data flows from processing → training → registry → endpoint, and MLOps tooling (Pipelines, Projects, Model Monitor) wraps that lifecycle for automation and governance.

What This Cheat Sheet Covers

This topic spans 19 focused tables and 141 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: SageMaker Studio and Development EnvironmentsTable 2: Training Jobs and the Estimator APITable 3: Distributed TrainingTable 4: Hyperparameter TuningTable 5: SageMaker Processing JobsTable 6: SageMaker Pipelines and MLOps WorkflowTable 7: Model RegistryTable 8: Inference Endpoint TypesTable 9: Advanced Endpoint PatternsTable 10: SageMaker Feature StoreTable 11: SageMaker Clarify — Bias and ExplainabilityTable 12: SageMaker Model MonitorTable 13: SageMaker Experiments and LineageTable 14: SageMaker JumpStartTable 15: SageMaker Autopilot and Canvas (AutoML)Table 16: SageMaker Data WranglerTable 17: SageMaker Ground Truth — Data LabelingTable 18: SageMaker MLOps and ProjectsTable 19: SageMaker Built-in Algorithms

Table 1: SageMaker Studio and Development Environments

SageMaker Studio is the primary integrated development environment for the platform, providing a browser-based workspace where data scientists and engineers access notebooks, experiments, pipelines, and model governance in one place. Understanding the Studio architecture — domains, user profiles, and application types — is foundational for configuring team access and customizing compute environments.

Feature	Example	Description
SageMaker Unified Studio	Single browser IDE for SQL, notebooks, pipelines, MLflow	Unified IDE that consolidates data engineering, ML development, and generative AI workflows into one experience with access to all AWS analytics and AI services.
Domain	`CreateDomain` with VPC, auth mode, default settings	Top-level organizational unit that scopes a team's Studio environment; contains user profiles, apps, and shared resources within a VPC.
UserProfile	`CreateUserProfile` with per-user execution role	Per-user configuration within a domain that can override domain defaults for instance types, images, and execution roles.
JupyterLab Space	Persistent workspace with kernel gateway app	Private or shared compute environment running JupyterLab; persists notebooks and installed packages between sessions.
Code Editor (VS Code)	Connect local VS Code / Cursor to SageMaker compute	Browser or local IDE experience powered by Code-OSS; supports local IDE connections via AWS Toolkit so you use your own editor against SageMaker compute.

Table 1: SageMaker Studio and Development Environments

Feature	Example	Description
SageMaker Unified Studio	Single browser IDE for SQL, notebooks, pipelines, MLflow	Unified IDE that consolidates data engineering, ML development, and generative AI workflows into one experience with access to all AWS analytics and AI services.
Domain	`CreateDomain` with VPC, auth mode, default settings	Top-level organizational unit that scopes a team's Studio environment; contains user profiles, apps, and shared resources within a VPC.
UserProfile	`CreateUserProfile` with per-user execution role	Per-user configuration within a domain that can override domain defaults for instance types, images, and execution roles.
JupyterLab Space	Persistent workspace with kernel gateway app	Private or shared compute environment running JupyterLab; persists notebooks and installed packages between sessions.
Code Editor (VS Code)	Connect local VS Code / Cursor to SageMaker compute	Browser or local IDE experience powered by Code-OSS; supports local IDE connections via AWS Toolkit so you use your own editor against SageMaker compute.