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Compare MLlib and scikit-learn 
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At a Glance
Market Segments
Mid-Market (50.0% of reviews)
Enterprise (40.7% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
MLlib
Star Rating
(14)4.1 out of 5
Market Segments
Mid-Market (50.0% of reviews)
Entry-Level Pricing
No pricing available
Learn more about MLlibscikit-learn
Star Rating
(59)4.8 out of 5
Market Segments
Enterprise (40.7% of reviews)
Entry-Level Pricing
No pricing available
Learn more about scikit-learnSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that MLlib's integration with Apache Spark allows for seamless processing of large datasets, making it a strong choice for big data applications, while scikit-learn is often praised for its simplicity and ease of integration with Python-based data science workflows.
- Reviewers mention that scikit-learn excels in its user-friendly API and extensive documentation, which significantly enhances the learning curve for new users, whereas MLlib's documentation can be less intuitive, leading to a steeper learning curve for beginners.
- G2 users highlight that scikit-learn offers a wider variety of algorithms and models, such as support vector machines and ensemble methods, which are readily accessible, while MLlib focuses more on distributed machine learning algorithms, which may not be necessary for all users.
- Users on G2 report that scikit-learn's ease of setup and administration is a major advantage, with many reviewers noting that they can get started quickly without extensive configuration, in contrast to MLlib, which may require more setup time due to its integration with Spark.
- Reviewers say that the quality of support for scikit-learn is notably higher, with many users appreciating the active community and responsive forums, while MLlib's support is often described as lacking in comparison, leading to frustration for users seeking help.
- Users mention that MLlib's ability to handle large-scale data processing is a significant benefit for enterprises dealing with massive datasets, while scikit-learn is often favored by smaller teams and individual data scientists for its lightweight nature and ease of use.
Pricing
Entry-Level Pricing
MLlib
No pricing availablescikit-learn
No pricing availableFree Trial
MLlib
No trial information availablescikit-learn
No trial information availableRatings
Meets Requirements
8.5
14
9.6
52
Ease of Use
8.8
14
9.6
52
Ease of Setup
8.7
9
9.6
40
Ease of Admin
7.9
7
9.4
39
Quality of Support
7.3
10
9.4
48
Has the product been a good partner in doing business?
7.6
7
9.2
35
Product Direction (% positive)
7.5
14
9.3
52
Features by Category
Integration - Machine Learning
Not enough data
Not enough data
Learning - Machine Learning
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
MLlib and scikit-learn are categorized as Machine Learning
Unique Categories
MLlib has no unique categories
scikit-learn has no unique categories
Reviews
Reviewers' Company Size
MLlib
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
scikit-learn
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Financial Services
21.4%
Computer Software
21.4%
Telecommunications
14.3%
Information Technology and Services
14.3%
Wireless
7.1%
Other
21.4%
Computer Software
35.6%
Information Technology and Services
16.9%
Higher Education
10.2%
Computer & Network Security
6.8%
Hospital & Health Care
5.1%
Other
25.4%
Alternatives
Discussions
MLlib has no discussions with answers
What is scikit-learn used for?
2 Comments
MS
Scikit-learn is a powerful library, well-integrated with other Python libraries such as pandas, NumPy, Matplotlib, and Seaborn. It supports creating machine...Read more
What is Python Scikit learn?
1 Comment
RA
It is a library used to implement machine-learning models. Provides vast range of methods to perform data preprocessing, feature selection, and popularly...Read more
scikit-learn has no more discussions with answers
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