What is MLeap?
MLeap is a common serialization format and execution engine for machine learning pipelines. It supports Spark, Scikit-learn and Tensorflow for training pipelines and exporting them to an MLeap Bundle. Serialized pipelines (bundles) can be deserialized back into Spark, Scikit-learn, TensorFlow graphs, or an MLeap pipeline for use in a scoring engine (API Servers).
Many companies that use Spark and Scikit-learn have a difficult time deploying their research ML/data pipelines models to production API services. Even using Tensorflow can be difficult to set these services up if a company does not wish to use Python in their API stack or does not use Google ML Cloud. MLeap provides simple interfaces to execute entire ML pipelines, from feature transformers to classifiers, regressions, clustering algorithms, and neural networks.
Your models are your models. Take them with you wherever you go using MLeap Bundles. Platforms like Microsoft Azure and Google ML can lock you into their services package. MLeap allows you to take your models with you wherever you go.
Spark, Scikit-learn and Tensorflow: One Runtime
Mixing and matching ML technologies becomes a simple task. Instead of requiring an entire team of developers to make research pipelines production ready, simply export to an MLeap Bundle and run your pipeline wherever it is needed.
Other benefits of a unified runtime:
- Train different pieces of your pipeline using Spark, Scikit-learn or Tensorflow, then export them to one MLeap Bundle file and deploy it anywhere
- If you're using Scikit for R&D, but Spark comes out with a better algorithm, you can export your Scikit ML pipeline to Spark, train the new model in Spark and then deploy to production using the MLeap runtime
In addition to providing a useful execution engine, MLeap Bundles provide a common serialization format for a large set of ML feature extractors and algorithms that are able to be exported and imported across Spark, Scikit-learn, Tensorflow and MLeap. This means you can easily convert pipelines between these technologies depending on where you need to execute a pipeline.
For the most part, we do not modify any internal code or require custom implementations of transformers in any Spark or Scikit-learn. For Tensorflow, we use as many builtin ops as we can and implement custom ops for MLeap when they do not exist. This means that code changes to your existing pipelines are minimal to get up and running with MLeap. For many use cases, no changes will be required and you can simply export to an MLeap Bundle or deploy to a Combust API server to start getting immediate use of your pipeline.