{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Tracks machine learning experiments, trials, and trial components in AWS SageMaker training jobs, processing jobs, and notebooks.","skillfed_tags":["aws-sagemaker","experiment-tracking","ml-workflow"],"use_cases":["Log and organize multiple training runs in SageMaker notebooks to compare hyperparameters and model performance.","Track multi-step ML workflows where each step (preprocessing, training, evaluation) is a separate trial component.","Record experiment metadata from inside running SageMaker training and processing jobs for later analysis.","Build experiment lineage and audit trails for reproducibility in production ML pipelines.","Query and analyze experiment results using the ExperimentAnalytics interface to extract metrics across trials."],"what_it_does":"sagemaker-experiments is a Python SDK for tracking machine learning experiments within AWS SageMaker. It provides a high-level interface to organize and log experiment metadata\u2014experiments (collections of trials), trials (multi-step workflows), and trial components (individual steps like data cleaning or model training)\u2014directly from Python scripts, notebooks, and SageMaker jobs. The package depends on boto3 and offers a Tracker context manager for logging trial component information.\n\nThe package is designed to integrate with SageMaker Studio and enable comparison of multiple training runs. However, the description explicitly notes that the official SageMaker SDK is now the recommended path and this repository may not reflect the latest product improvements. It remains useful for legacy workflows or direct experiment tracking in existing SageMaker setups.","worth_installing":"Yes, if you are already using SageMaker and need lightweight experiment tracking in existing jobs or notebooks. No, if you are starting a new SageMaker project\u2014use the official SageMaker SDK instead, as this package is dormant (last update 2023-05-17) and the description recommends the SDK for up-to-date features. Install only for legacy codebases or when you specifically need the standalone Tracker interface."},"id":"sagemaker-experiments","links":{"html":"https://skillfed.io/packages/sagemaker-experiments","md":"https://skillfed.io/packages/sagemaker-experiments.md","pypi":"https://pypi.org/project/sagemaker-experiments/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-05-17","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-experiments","python_support":"unspecified","summary":"Open source library for Experiment Tracking in SageMaker Jobs and Notebooks"},"popularity":{"monthly_downloads":130247,"position":11648,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.45"}