scikit-learn VS MLflow

Compare scikit-learn vs MLflow and see what are their differences.

scikit-learn

scikit-learn: machine learning in Python (by scikit-learn)

MLflow

Open source platform for the machine learning lifecycle (by mlflow)
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scikit-learn MLflow
24 19
48,142 10,823
0.8% 3.0%
9.9 9.7
3 days ago 1 day ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

scikit-learn

Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-13.

MLflow

Posts with mentions or reviews of MLflow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-06.

What are some alternatives?

When comparing scikit-learn and MLflow you can also consider the following projects:

Keras - Deep Learning for humans

Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

Surprise - A Python scikit for building and analyzing recommender systems

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

tensorflow - An Open Source Machine Learning Framework for Everyone

dvc - πŸ¦‰Data Version Control | Git for Data & Models | ML Experiments Management

gensim - Topic Modelling for Humans

PyBrain

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.