scikit-learn vs MLflow

Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

MLflow

Stacks227

Followers524

Votes9

GitHub Stars22.8K

Forks5.0K

MLflow vs scikit-learn: What are the differences?

Developers describe MLflow as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle. On the other hand, scikit-learn is detailed as "Easy-to-use and general-purpose machine learning in Python". scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

MLflow and scikit-learn can be categorized as "Machine Learning" tools.

MLflow and scikit-learn are both open source tools. It seems that scikit-learn with 36K GitHub stars and 17.6K forks on GitHub has more adoption than MLflow with 23 GitHub stars and 13 GitHub forks.

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Advice on scikit-learn, MLflow

cfvedova

Oct 10, 2020

Decided

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Detailed Comparison

scikit-learn	MLflow
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
-	Track experiments to record and compare parameters and results; Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production; Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms
Statistics
GitHub Stars 63.9K	GitHub Stars 22.8K
GitHub Forks 26.4K	GitHub Forks 5.0K
Stacks 1.3K	Stacks 227
Followers 1.1K	Followers 524
Votes 45	Votes 9
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	Pros 5 Code First 4 Simplified Logging

What are some alternatives to scikit-learn, MLflow?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.