AWS SageMaker for Data Scientists Cheat Sheet

Back to Data ScienceUpdated 2026-05-15

Amazon SageMaker is a fully managed machine learning service that enables data scientists to build, train, and deploy ML models at scale within a unified, production-ready environment. As part of AWS's broader AI platform, SageMaker provides an end-to-end workflow from data preparation through model deployment, removing the infrastructure overhead that traditionally slows down ML development. What makes SageMaker particularly valuable is its serverless orchestration — you define pipelines and training jobs, and AWS automatically provisions, scales, and shuts down compute resources as workload demands change, letting you focus entirely on optimizing model performance rather than managing clusters.

What This Cheat Sheet Covers

This topic spans 22 focused tables and 165 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: SageMaker Studio IDE ComponentsTable 2: Built-in Algorithms for Tabular DataTable 3: Training Job ConfigurationTable 4: SageMaker Pipelines for ML OrchestrationTable 5: SageMaker Feature StoreTable 6: SageMaker Model MonitorTable 7: SageMaker Clarify for Bias and ExplainabilityTable 8: Real-Time Inference EndpointsTable 9: Data Wrangler for No-Code Data PreparationTable 10: SageMaker Experiments for Run TrackingTable 11: SageMaker Canvas for No-Code AutoMLTable 12: Distributed Training StrategiesTable 13: Hyperparameter TuningTable 14: Batch Transform for Offline InferenceTable 15: Processing Jobs for Data/Model TasksTable 16: Model Registry and DeploymentTable 17: Asynchronous InferenceTable 18: SageMaker Neo for Model OptimizationTable 19: SageMaker JumpStartTable 20: Spot Training for Cost OptimizationTable 21: SageMaker Autopilot for AutoMLTable 22: Advanced Training Features

Table 1: SageMaker Studio IDE Components

Component	Example	Description
SageMaker Domain	`domain = session.create_domain(` `domain_name="ml-team"` `)`	Managed environment that provides authentication, authorization, and resource isolation for teams; acts as organizational boundary containing user profiles and shared storage.
User Profile	`profile = domain.create_user_profile(` `user_profile_name="data-scientist-1"` `)`	Individual workspace within a domain with dedicated storage and IAM execution role; each user gets isolated Jupyter environment.
JupyterLab Application	Launch JupyterLab 4 from Studio UI	Interactive notebook environment with support for Python, R, and custom kernels; provides code editing, debugging, and Git integration.
Kernel Gateway	Select `Python 3 (Data Science 3.0)` kernel	Compute backend that runs notebook cells; supports multiple kernel images with pre-installed ML frameworks.

Table 1: SageMaker Studio IDE Components

Component	Example	Description
SageMaker Domain	`domain = session.create_domain(` `domain_name="ml-team"` `)`	Managed environment that provides authentication, authorization, and resource isolation for teams; acts as organizational boundary containing user profiles and shared storage.
User Profile	`profile = domain.create_user_profile(` `user_profile_name="data-scientist-1"` `)`	Individual workspace within a domain with dedicated storage and IAM execution role; each user gets isolated Jupyter environment.
JupyterLab Application	Launch JupyterLab 4 from Studio UI	Interactive notebook environment with support for Python, R, and custom kernels; provides code editing, debugging, and Git integration.
Kernel Gateway	Select `Python 3 (Data Science 3.0)` kernel	Compute backend that runs notebook cells; supports multiple kernel images with pre-installed ML frameworks.