/ml_ops

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
• Deploy a MLflow Model to SageMaker
• Agent Evaluation and Model A/B Testing using MLflow
• SageMaker HPO with MLflow
• SageMaker Pipelines with MLflow
• How to Setup Amazon SageMaker with MLflow
• SageMaker Training with MLflow
• SageMaker Data Quality Model Monitor for Batch Transform with SageMaker Pipelines On-demand
• SageMaker Model Quality Model Monitor for Batch Transform With SageMaker Pipelines On-demand
• Basic Pipeline for Batch Inference using Low-code Experience for SageMaker Pipelines
• Glue ETL as part of a SageMaker pipeline
• SageMaker Pipelines integration with Model Monitor and Clarify
• Using @step Decorated Step with EMR Step
• SageMaker Pipelines Lambda Step
• Launch Amazon SageMaker Autopilot experiments directly from within Amazon SageMaker Pipelines to easily automate MLOps workflows
• SageMaker Pipeline - Local Mode
• Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines
• SageMaker Pipelines: Selective Execution Demo
• Use SageMaker Pipelines With Step Caching
• Quick Start - Introducing @step Decorator and Pipeline Trigger
• Quick Start - Using @step Decorated Step with Classic TrainingStep
• Quick Start - Using @step Decorated Steps with ConditionStep
• SageMaker Pipelines EMR Step With Running EMR Cluster
• SageMaker Pipelines EMR Step With Cluster Lifecycle Management
• SageMaker Pipelines Tuning Step
• Use SageMaker Pipelines to Run Your Jobs Locally
• SageMaker Pipelines