{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Builds and orchestrates machine learning workflows on AWS Step Functions and SageMaker directly from Python code, without manual AWS service provisioning.","skillfed_tags":["aws-integration","workflow-orchestration","ml-pipeline"],"use_cases":["Build end-to-end SageMaker training pipelines in Python that automatically handle data preprocessing, model training, and deployment steps.","Orchestrate multi-step ML workflows combining Lambda functions, SageMaker endpoints, and ECS tasks without manual AWS service integration.","Create reusable ML workflow templates in Jupyter notebooks and deploy them as managed state machines for team use.","Implement retry logic, error handling, and conditional branching in ML pipelines without writing AWS CloudFormation or Step Functions JSON.","Monitor and re-run ML workflow executions from Python code, tracking job history and debugging failures in the Step Functions console."],"what_it_does":"The AWS Step Functions Data Science SDK is a Python library that lets you define machine learning workflows in code and deploy them to AWS Step Functions for cloud execution. Instead of manually configuring AWS services, you write Python to create steps (Pass, Wait, Lambda, SageMaker tasks, etc.), chain them into workflows, and then upload and run them in the AWS cloud. Workflows are defined locally in Python or Jupyter notebooks but execute as managed state machines in AWS Step Functions, allowing you to orchestrate SageMaker training, Lambda functions, and other AWS services at scale.\n\nThe package provides a high-level API to construct steps, define retry and error-handling logic, inspect workflow definitions, and trigger executions from Python. Once deployed to Step Functions, workflows can be re-executed multiple times with different inputs, and execution history is tracked in the AWS console or queried via the SDK. The library is designed for data scientists who want to build production ML pipelines without deep AWS infrastructure knowledge.","worth_installing":"No. The package has not been released since January 2022 and is in aging maintenance status. While it has no known vulnerabilities and carries a permissive Apache 2.0 license, the three-year gap between the last release and current date creates significant risk of API drift with AWS services. Python 3.6 support is explicitly listed but current Python version compatibility is unspecified. For new ML workflow automation on AWS, consider using boto3 directly or AWS's more recently maintained SDKs and CDK constructs."},"id":"stepfunctions","links":{"html":"https://skillfed.io/packages/stepfunctions","md":"https://skillfed.io/packages/stepfunctions.md","pypi":"https://pypi.org/project/stepfunctions/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2022-01-24","license_spdx":null,"license_treatment":"permissive","name":"stepfunctions","python_support":"unspecified","summary":"Open source library for developing data science workflows on AWS Step Functions."},"popularity":{"monthly_downloads":152392,"position":10903,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.0"}