combust/mleap

Health v2 · maintenance only

Adoption not part of the score

1543 stars · 316 forks observed · 2026-08-28

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

MLeap is a serialization format (Bundle.ML) and portable execution engine for machine learning pipelines, implemented in Scala with Python support. It lets you train pipelines in Spark, Scikit-learn, or TensorFlow, export them once, and run them on a lightweight JVM runtime without dependencies on Spark, sklearn, numpy, or pandas.

Use cases

• deploy spark ml pipelines to production without a spark cluster
• serve scikit-learn models from a jvm api service
• export trained ml pipelines to a portable format for realtime scoring
• run ml feature transformers and models without python dependencies
• serialize ml pipelines as or protobuf bundles
• mix spark and scikit-learn trained components in one deployable pipeline
• score models in batch mode by deserializing bundles back into spark

When to choose

• you train models in Spark or Scikit-learn but serve them from JVM-based services
• you need low-latency realtime scoring without Spark runtime overhead
• you want portable model bundles not locked to a cloud ML platform
• your production stack is JVM-based and cannot depend on Python

When to avoid

• your models are deep learning focused with heavy TensorFlow/PyTorch serving needs better served by dedicated serving stacks
• your entire stack is Python and you can serve sklearn models directly
• you rely on custom transformers not supported by MLeap's transformer set
• you need GPU-accelerated inference

Facets

library · maturity active

serialization machine-learning etl machine-learning big-data developer-tools jvm python cross-platform spark scikit-learn tensorflow ml-pipelines model-deployment bundle-ml protobuf realtime-scoring data-engineering

Member repositories

For agents

markdown · JSON · MCP: product_card(name="combust/mleap")