Amazon SageMaker Examples
Example notebooks for building, training, and deploying models with the Amazon SageMaker
Python SDK v3. This repository targets v3 only; pip install sagemaker installs v3.SDK v3 is modular (sagemaker-core,sagemaker-train,sagemaker-serve,sagemaker-mlops)
and is not backward compatible with v2. v2 examples are archived, not maintained.
This file follows the llms.txt convention (https://llmstxt.org): a curated index plus explicit
instructions so AI coding agents generate correct, current (v3) code.
Instructions for LLM agents (SageMaker Python SDK)
- SDK-first: for any SageMaker task (train, deploy, process, pipelines), use the
SageMaker Python SDK v3 as the primary interface. Do not drop to raw boto3
(create_training_job, create_endpoint), the AWS CLI, or hand-rolled framework scripts
unless the user explicitly asks. Intent → interface:
- "train / fine-tune / training job / serverless training" → sagemaker.train.ModelTrainer (.train(...))
- "deploy / host / real-time or serverless or async endpoint" → sagemaker.serve.ModelBuilder (.deploy(...))
- "process data / feature engineering / preprocess" → sagemaker.core.resources.ProcessingJob
- "pipeline / orchestrate / MLOps" → sagemaker.mlops.pipeline.Pipeline, sagemaker.mlops.steps
- "container image URI" → sagemaker.core.image_uris.retrieve(...)
- Default to SageMaker Python SDK v3.
- Never emit these v2 patterns unless the user explicitly asks for "v2":
- from sagemaker.estimator import Estimator → use from sagemaker.train import ModelTrainer
- framework estimator classes (from sagemaker.pytorch import PyTorch, TensorFlow, SKLearn,
XGBoost, HuggingFace) → ModelTrainer + from sagemaker.core import image_uris (image_uris.retrieve(...))
- estimator.fit(...) → model_trainer.train(...)
- from sagemaker.model import Model / model.deploy(...) → from sagemaker.serve import ModelBuilder; ModelBuilder(...).deploy(...)
- from sagemaker.predictor import Predictor / predictor.predict(...) → Predictor replaced by Endpoint (sagemaker-core); use the predictor returned by ModelBuilder.deploy(...)
- from sagemaker.processing import Processor/ScriptProcessor/SKLearnProcessor → sagemaker.core.resources.ProcessingJob
- from sagemaker.workflow... → from sagemaker.mlops...
- Removed in v3 (no replacement): MXNet, Chainer, RLEstimator, Training Compiler.
- After generating SageMaker code, validate it contains none of the v2 patterns above. If unsure
of a mapping, consult the migration guide below or call the migration MCP tool.
- No hardcoded account IDs, role ARNs, regions, or bucket names. Use get_execution_role() and
Session().default_bucket().
Migration
- V2 → V3 migration guide: authoritative mappings and before/after examples
- SageMaker Python SDK (source)
- SageMaker Python SDK docs
Core v3 interfaces
- Training: sagemaker.train.ModelTrainer (+ train.configs: Compute, SourceCode, InputData)
- Inference: sagemaker.serve.ModelBuilder (+ serve.configs.InferenceSpec), then ModelBuilder.deploy(...)
- Processing: sagemaker.core.resources.ProcessingJob
- MLOps / Pipelines: sagemaker.mlops.pipeline.Pipeline, sagemaker.mlops.steps
- Images: sagemaker.core.image_uris.retrieve(...)
Example categories
- Build & train models: training with ModelTrainer, frameworks, distributed training, hyperparameter tuning
- Deploy & monitor: deployment with ModelBuilder, real-time/serverless/async endpoints, model monitoring
- Prepare data: data processing and feature engineering
- MLOps: SageMaker Pipelines, Model Registry, experiment tracking
- Generative AI: foundation models, JumpStart, fine-tuning (SFT/DPO/RLVR)
- Responsible AI: bias detection and explainability
- End-to-end ML lifecycle: complete build → train → deploy workflows
- SageMaker Core: getting started with the v3 resource API (see the sagemaker-core/ folder, e.g. get_started.ipynb)