# Amazon SageMaker Examples

### ML Ops

These examples showcases Amazon SageMaker's features to implement machine learning models in production environments with continuous integration and deployment.

- [Model Lineage Tracking](sm-ml_lineage_tracking_model_governance_graph/sm-ml_lineage_tracking_model_governance_graph.ipynb)
- [Deploy a MLflow Model to SageMaker](sm-mlflow_deployment/sm-mlflow_deployment.ipynb)
- [Agent Evaluation and Model A/B Testing using MLflow](sm-mlflow_eval/strands-agents-sagemaker-model-evaluation.ipynb)
- [SageMaker HPO with MLflow](sm-mlflow_hpo/sm-mlflow_hpo.ipynb)
- [SageMaker Pipelines with MLflow](sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb)
- [How to Setup Amazon SageMaker with MLflow](sm-mlflow_setup/sm-mlflow_setup.ipynb)
- [SageMaker Training with MLflow](sm-mlflow_training/sm-mlflow_training.ipynb)
- [SageMaker Data Quality Model Monitor for Batch Transform with SageMaker Pipelines On-demand](sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand.ipynb)
- [SageMaker Model Quality Model Monitor for Batch Transform With SageMaker Pipelines On-demand](sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand.ipynb)
- [Basic Pipeline for Batch Inference using Low-code Experience for SageMaker Pipelines](sm-pipelines_batch_inference_step_decorator/sm-pipelines_batch_inference_step_decorator.ipynb)
- [Glue ETL as part of a SageMaker pipeline](sm-pipelines_callback_step/sm-pipelines_callback_step.ipynb)
- [SageMaker Pipelines integration with Model Monitor and Clarify](sm-pipelines_clarify_model_monitor_integration/sm-pipelines_clarify_model_monitor_integration.ipynb)
- [Using @step Decorated Step with EMR Step](sm-pipelines_emr_step_using_step_decorator/sm-pipelines_emr_step_using_step_decorator.ipynb)
- [SageMaker Pipelines Lambda Step](sm-pipelines_lambda_step/sm-pipelines_lambda_step.ipynb)
- [Launch Amazon SageMaker Autopilot experiments directly from within Amazon SageMaker Pipelines to easily automate MLOps workflows](sm-pipelines_launching_autopilot_with_auto_ml_step/sm-pipelines_launching_autopilot_with_auto_ml_step.ipynb)
- [SageMaker Pipeline - Local Mode](sm-pipelines_local_mode/sm-pipelines_local_mode.ipynb)
- [Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines](sm-pipelines_preprocess_train_evaluate_batch_transform/sm-pipelines_preprocess_train_evaluate_batch_transform.ipynb)
- [SageMaker Pipelines: Selective Execution Demo](sm-pipelines_selective_execution/sm-pipelines_selective_execution.ipynb)
- [Use SageMaker Pipelines With Step Caching](sm-pipelines_step_caching/sm-pipelines_step_caching.ipynb)
- [Quick Start - Introducing @step Decorator and Pipeline Trigger](sm-pipelines_step_decorator/sm-pipelines_step_decorator.ipynb)
- [Quick Start - Using @step Decorated Step with Classic TrainingStep](sm-pipelines_step_decorator_with_classic_training_step/sm-pipelines_step_decorator_with_classic_training_step.ipynb)
- [Quick Start - Using @step Decorated Steps with ConditionStep](sm-pipelines_step_decorator_with_condition_step/sm-pipelines_step_decorator_with_condition_step.ipynb)
- [SageMaker Pipelines EMR Step With Running EMR Cluster](sm-pipelines_emr_step_with_running_emr_cluster.ipynb)
- [SageMaker Pipelines EMR Step With Cluster Lifecycle Management](sm-pipelines_emr-step-with_cluster_lifecycle_management.ipynb)
- [SageMaker Pipelines Tuning Step](sm-pipelines_hyperparameter_tuning.ipynb)
- [Use SageMaker Pipelines to Run Your Jobs Locally](sm-pipelines_local_mode.ipynb)
- [SageMaker Pipelines](sm-pipelines_train_model_registry_deploy.ipynb)