ICT-BDA/EasyML

Health v2 · maintenance only

Adoption not part of the score

1976 stars · 434 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

application · maturity maintenance

machine-learning data-science workflow-automation gui etl machine-learning big-data data-science self-hosted jvm self-hosted visual-programming dag-workflow spark hadoop ml-studio drag-and-drop docker linux web-server

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markdown · JSON · MCP: product_card(name="ICT-BDA/EasyML")