# ICT-BDA/EasyML

Easy Machine Learning is a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real world tasks.

Repository: https://github.com/ICT-BDA/EasyML
Canonical: https://ross.abutalabs.com/products/easyml
Language: Java
License: Apache-2.0
License Family: permissive
Topics: machine-learning-studio, machine-learning-platform, learning-platform, machine-learning, big-data-analytics
Last push: 2023-12-18T03:17:14+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3435, "days_push": 989, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1976, forks 434 (observed 2026-08-28T04:06:01.338080+00:00)

## What it is
EasyML is a general-purpose dataflow-based machine learning platform where tasks are defined as directed acyclic graphs of operations. It includes a distributed ML library built on Spark, a drag-and-drop GUI studio for building and monitoring tasks, and a cloud execution service running on Hadoop/Spark clusters deployed with Docker.

## Use cases
- build machine learning pipelines visually with drag-and-drop
- run distributed ML algorithms on Spark without writing code
- create and share reusable ML workflow DAG templates
- orchestrate multi-step data preprocessing and model training tasks
- teach machine learning through a visual studio interface
- schedule ML task nodes automatically based on data dependencies

## When to choose
- you want a visual, no-code interface for composing ML workflows
- your algorithms and data already live on Hadoop/Spark clusters
- you need to share and reuse ML pipelines and experiment results across a team
- you want to mix standalone and distributed algorithms in one workflow

## When to avoid
- you need a modern, actively developed ML platform with recent community support
- you prefer code-first workflows like Python notebooks or Kubeflow pipelines
- your stack is not Java/Hadoop/Spark based
- you need deep learning or GPU training support

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, data-science, workflow-automation, gui, etl
- domain: machine-learning, big-data, data-science, self-hosted
- platform: jvm, self-hosted
- tags: visual-programming, dag-workflow, spark, hadoop, ml-studio, drag-and-drop, docker, linux, web-server

## Member repositories
- ICT-BDA/EasyML (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.338080+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 2026-08-30T03:04:48.665736+00:00, confidence not recorded.
  - readme: https://github.com/ICT-BDA/EasyML (fetched 2026-08-28T04:06:01.338080+00:00, sha ef416032c754)
- Data as of 2026-08-30T08:39:29.467469+00:00.