Sitemap & RSS Feed Tags

Getting started with MLeap and Scikit-Learn

Deploying machine learning data pipelines and algorithms should not be a time-consuming or difficult task. MLeap allows data scientists and engineers to deploy machine learning pipelines from Spark and Scikit-learn to a portable format and execution engine (MLeap documentation).

MLeap helps you to serialize and deserialize your model/pipeline. It exists for Spark, Scikit-Learn and Tensorflow. You can use it with Scikit-Learn but the documentation is quite dry for the moment. This is why I decided to do this small tutorial.

Prepare some data

I will use Numpy and Pandas to get some data.

Suppose you want to do a classification. You need a dataframe with a feature column and a label column.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(100, 1), columns=['a'])

df["y"] = (df['a'] > 0.5).astype(int)


You might have a result like that:

a y
0.61 1
0.26 0
0.20 0

A dataframe with variable a as a feature column and y as a label column for a classification.

Serialize with MLeap and Scikit-Learn

I will use a logistic regression.

from mleap.sklearn.logistic import LogisticRegression

logistic_regression = LogisticRegression(fit_intercept=True)

                           prediction_column='e_binary')[['a']], df[['y']])


In mleaptestmodel folder, you have different files. They represent your model in a serialized format.

Deserialize with MLeap and Scikit-Learn

To get back the model and use it for a prediction, you can do that:

from mleap.sklearn.logistic import LogisticRegression

node_name = "{}.node".format("model.json")
logistic_regression_tf = LogisticRegression()

model = logistic_regression_tf.deserialize_from_bundle(

expected = model.predict(df[["a"]])


MLeap seems to be a nice project but not very active. I’m not sure I would recommend it. But at least, you’ve got a getting started with logistic regression if you must deal with it.

For the moment, the documentation is quite dry. For instance, I found nothing about deserializing with Scikit-Learn.

The tests of the project help me to understand this part.

I plan to make a pull request to add some documentation.

Thank you for reading.