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Compare V7 Darwin and scikit-learn 
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At a Glance
Market Segments
Small-Business (55.8% of reviews)
Enterprise (40.7% of reviews)
Entry-Level Pricing
Free
No pricing available
V7 Darwin
Star Rating
(54)4.8 out of 5
Market Segments
Small-Business (55.8% of reviews)
Entry-Level Pricing
Free
Browse all 4 pricing plansscikit-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 scikit-learn excels in model training efficiency, with a perfect score of 10.0, making it a preferred choice for those focused on developing machine learning models quickly. In contrast, V7 also scores highly in model training but emphasizes its pre-built algorithms, which some users find beneficial for rapid deployment.
- Reviewers mention that V7 shines in its user-friendly interface, particularly with its drag-and-drop functionality, which simplifies the data labeling workflow. Scikit-learn, while powerful, is often noted for its steeper learning curve, which can be a barrier for new users.
- G2 users highlight V7's strong support for human-in-the-loop processes, scoring 9.4 in labeler quality, which enhances the accuracy of machine learning models. Users on G2 have noted that scikit-learn lacks this level of integrated support, making it less ideal for projects requiring extensive human oversight.
- Users say that V7's automation features, such as automatic routing of labeling tasks, significantly improve operational efficiency, scoring 9.5 in this area. In comparison, scikit-learn does not offer similar automation capabilities, which can lead to more manual intervention in workflows.
- Reviewers mention that scikit-learn's flexibility in framework support and versioning is a major advantage for developers who need to integrate with various systems. However, V7's managed service model, scoring a perfect 10.0, is praised for its ease of deployment, making it a strong contender for businesses looking for a hassle-free setup.
- Users report that V7's comprehensive data quality features, including actionable insights for data quality, are highly valued, with a score of 9.3. In contrast, scikit-learn's focus is more on algorithmic performance rather than data management, which some users find limiting for end-to-end project needs.
Pricing
Entry-Level Pricing
V7 Darwin
Free Plan
Free
Try out the core functionalities and set up your workflow for free.
- 1 Workspace
- 10 User Licenses
- 3 Integrated Models
scikit-learn
No pricing availableFree Trial
V7 Darwin
Free Trial is available
scikit-learn
No trial information availableRatings
Meets Requirements
9.5
38
9.6
52
Ease of Use
9.5
38
9.6
52
Ease of Setup
9.5
17
9.6
40
Ease of Admin
9.4
15
9.4
39
Quality of Support
9.6
36
9.4
48
Has the product been a good partner in doing business?
9.9
14
9.2
35
Product Direction (% positive)
9.6
32
9.3
52
Features by Category
Deployment
Not enough data
Not enough data
9.4
6
Not enough data
9.7
5
Not enough data
9.0
7
Not enough data
9.8
7
Not enough data
Deployment
Not enough data
Not enough data
9.3
5
Not enough data
9.7
6
Not enough data
9.2
6
Not enough data
9.8
7
Not enough data
Management
9.3
5
Not enough data
10.0
6
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Operations
9.7
6
Not enough data
Not enough data
Not enough data
10.0
6
Not enough data
Management
10.0
5
Not enough data
9.7
5
Not enough data
9.3
5
Not enough data
Generative AI
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Quality
9.4
21
Not enough data
9.5
24
Not enough data
9.4
21
Not enough data
9.3
22
Not enough data
Automation
9.4
16
Not enough data
9.4
14
Not enough data
Image Annotation
9.3
27
Not enough data
9.4
24
Not enough data
9.1
17
Not enough data
9.2
18
Not enough data
Natural Language Annotation
9.1
13
Not enough data
8.5
9
Not enough data
9.0
10
Not enough data
Speech Annotation
7.7
8
Not enough data
7.5
8
Not enough data
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
V7 Darwin and scikit-learn share no categories
Unique Categories
V7 Darwin is categorized as Data Labeling and MLOps Platforms
scikit-learn is categorized as Machine Learning
Reviews
Reviewers' Company Size
V7 Darwin
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
Information Technology and Services
25.0%
Computer Software
19.2%
Research
7.7%
Hospital & Health Care
5.8%
Industrial Automation
3.8%
Other
38.5%
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%
Discussions
V7 Darwin 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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