# sematic-ai/sematic

An open-source ML pipeline development platform

Repository: https://github.com/sematic-ai/sematic
Canonical: https://ross.abutalabs.com/products/sematic
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai, data-science, machine-learning, ml, ml-ops, ml-pipeline, ml-pipelines, mlops, pipeline, python, python3
Last push: 2025-01-09T17:56:08+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 28, longevity 100
- inputs: {"age_days": 1597, "days_push": 601, "days_rel": 601, "gap_med": 53.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1001, forks 63 (observed 2026-09-03T02:15:17.773846+00:00)

## What it is
Sematic is an open-source ML pipeline development platform that lets engineers write end-to-end pipelines in pure Python. Pipelines can run locally on a laptop or on Kubernetes/cloud, with type-safe, traceable, reproducible execution and a web dashboard for monitoring and visualizing artifacts.

## Use cases
- build ml pipelines in python
- orchestrate model training and data processing jobs
- run ml pipelines locally then on kubernetes
- track and visualize pipeline artifacts
- make ml workflows reproducible and type-safe
- chain spark jobs with pytorch training
- monitor ml pipeline runs in a dashboard

## When to choose
- you want to author ML pipelines in pure Python without YAML or DSLs
- you need local-to-cloud parity for pipeline execution
- you want artifact tracking, lineage, and reproducibility out of the box
- you need to mix heterogeneous compute like Spark, GPUs, and CPUs per step

## When to avoid
- you need a battle-tested orchestrator with a large community like Airflow or Kubeflow
- your workflows are not ML/data-science oriented
- you need real-time streaming pipelines rather than batch jobs

## Facets
- artifact type: framework
- maturity: active
- function: workflow-automation, scheduling, monitoring, data-science, machine-learning, developer-tools
- domain: machine-learning, data-science
- platform: python, cross-platform, cli
- tags: mlops, ml-pipelines, pipeline-orchestration, reproducibility, lineage-tracking, web-dashboard, local-to-cloud, pipelines, devops, kubernetes, docker

## Member repositories
- sematic-ai/sematic (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:17.773846+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:16:09.178135+00:00, confidence not recorded.
  - readme: https://github.com/sematic-ai/sematic (fetched 2026-09-03T02:15:17.773846+00:00, sha 05ead300cf63)
- Data as of 2026-08-30T08:39:29.467469+00:00.