{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"A framework for building, prototyping, and deploying AI and ML workflows from notebooks to production, handling data management, compute scaling, and orchestration across local and cloud environments.","skillfed_tags":["ml-orchestration","workflow-framework","distributed-computing"],"use_cases":["Prototype ML models locally in a notebook, then scale the same code to distributed cloud compute without rewriting","Track and version experiments across multiple runs, comparing results and managing artifacts automatically","Deploy a trained model as a production workflow that triggers on events and scales based on demand","Run embarrassingly parallel data processing jobs across many machines with built-in failure recovery","Manage dependencies and orchestrate multi-stage pipelines with clear data lineage between steps"],"what_it_does":"ob-metaflow is a framework for building AI and ML systems that spans the entire lifecycle from local prototyping in notebooks to reliable production deployments. It unifies code, data, and compute management, providing a Pythonic API for experiment tracking, versioning, and visualization alongside support for scaling workloads horizontally and vertically across cloud infrastructure using CPUs and GPUs.\n\nThe framework handles dependency management, data access, and orchestration, with built-in support for both embarrassingly parallel and gang-scheduled compute jobs. It integrates with cloud providers via boto3 and kubernetes, and supports deployment to production orchestrators with reactive event triggering. Originally developed at Netflix and now supported by Outerbounds, it is designed for teams ranging from individual researchers to large organizations running thousands of concurrent workflows.","worth_installing":"Yes. ob-metaflow is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive license. It is well-suited for teams building ML systems that need to move from prototyping to production without significant refactoring. Start with local prototyping and scale incrementally as your infrastructure needs grow."},"id":"ob-metaflow","links":{"html":"https://skillfed.io/packages/ob-metaflow","md":"https://skillfed.io/packages/ob-metaflow.md","pypi":"https://pypi.org/project/ob-metaflow/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"ob-metaflow","python_support":"unspecified","summary":"Metaflow: More AI and ML, Less Engineering"},"popularity":{"monthly_downloads":113330,"position":12344,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.19.37.1"}