MLeap
MLeap is a portable execution engine for deploying machine learning pipelines from Spark and Scikit-learn without their runtime dependencies.
What is MLeap?
MLeap is an execution engine and serialization library that allows machine learning pipelines trained in Apache Spark or Scikit-learn to be exported to a portable format and run in production without their original dependencies. It solves the problem of deploying ML models by providing a lightweight runtime that eliminates the need for Spark Context or Python scientific stacks in serving environments.
Data scientists and ML engineers who train models in Spark or Scikit-learn and need to deploy them into production systems where framework dependencies are impractical.
Developers choose MLeap because it provides a unified, dependency-free runtime for models from multiple frameworks, enabling faster and more portable ML deployments with maintained parity to training environments.
Overview
MLeap: Deploy ML Pipelines to Production
Use Cases
Best For
- Deploying Spark ML pipelines to microservices without Spark dependencies
- Serving Scikit-learn models in JVM-based production environments
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