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Scikit-learn VS Atlassian Design

Compare Scikit-learn VS Atlassian Design and see what are their differences

Kimp
Kimp is an unlimited design company, specializing in graphic design (including print and digital designs, custom illustrations, landing page designs and email designs) and video design. featured
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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Atlassian Design logo Atlassian Design

Design, develop, and deliver
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Atlassian Design Landing page
    Landing page //
    2023-06-22

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Atlassian Design features and specs

  • Comprehensive Design System
    Atlassian Design provides a complete and consistent design system for building applications, which helps ensure user interfaces are coherent and professional.
  • Access to Components
    It offers a wide range of pre-built UI components that can be easily integrated into projects, saving time in the development process.
  • Documentation
    Extensive and detailed documentation is available, which helps developers and designers understand how to use the system effectively.
  • Consistency
    Ensures that all components and patterns follow the same design principles, resulting in a more consistent user experience across different products.
  • Community Support
    Being a part of the broader Atlassian community means that there is a wealth of shared knowledge and resources available to help solve common problems.

Possible disadvantages of Atlassian Design

  • Learning Curve
    For new users, especially those not familiar with Atlassian products, the system can have a steep learning curve.
  • Customization Limitations
    While it provides many components, customization options might be limited for more unique or advanced use cases.
  • Dependency
    Relying heavily on Atlassian's design system means that changes or updates from Atlassian can impact your products, necessitating continuous adaptation.
  • Performance
    Using a large number of pre-built components might affect the performance of your application, especially if all components are not optimized for your specific use case.
  • Integration Complexity
    Integrating Atlassian Design with other systems or legacy codebases may require additional effort and potentially complex workarounds.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Analysis of Atlassian Design

Overall verdict

  • Yes, Atlassian Design is generally regarded as a good design system. Its emphasis on clarity, usability, and consistency makes it highly effective for teams looking to create seamless and user-friendly experiences.

Why this product is good

  • Atlassian Design is considered good because it provides a comprehensive and cohesive design system that ensures consistency across Atlassian's products. It is well-documented, user-focused, and continually updated to align with modern design trends and user needs. The platform offers a collection of guidelines, components, and patterns that facilitate the creation of intuitive and accessible user interfaces.

Recommended for

  • UI/UX Designers working on Atlassian products
  • Teams seeking guidance on design consistency
  • Product managers who prioritize a cohesive user experience
  • Developers implementing design systems
  • Design teams looking for a robust design framework

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Atlassian Design videos

+ Add

5 things our users want from the Atlassian Design System

More videos:

  • Review - Atlassian Design Week 2017

Category Popularity

0-100% (relative to Scikit-learn and Atlassian Design)

Scikit-learn

Atlassian Design

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Design Tools
0 0%
Design Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Color Palette Generator
0 0%
Color Palette Generator
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Atlassian Design

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Atlassian Design Reviews

We have no reviews of Atlassian Design yet.
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Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Atlassian Design. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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Atlassian Design mentions (12)

  • Top 5 Drag-and-Drop Libraries for React
    As the official evolution of react-beautiful-dnd, this library also comes with extensible accessibility features right out of the box. The default assistive controls are based on the Atlassian Design System, so if you’re already using that, integration will be seamless. But if you aren’t, you can easily replace those components with your own, or completely redefine how accessibility is provided and take a more... - Source: dev.to / 3 months ago
  • Getting Started with Color Module for Your Design System
    Atlassian Design System: Atlassian's Design System encompasses a color module encompassing primary, secondary, and functional colors, along with an extended palette for shades and tints. The system provides comprehensive guidelines for effective color usage and emphasizes accessibility. - Source: dev.to / over 1 year ago
  • Making a UI Kit. Is there a good checklist for Must Have elements?
    Atlassian design system: https://atlassian.design/. Source: about 2 years ago
  • What's the best way to encapsulate a feature to make it reusable?
    Regarding discoverability, you could build a directory with documentation. Similarly to how design systems are documented, e.g: https://atlassian.design/ But if you really want to share them you'll probably need to evangelize it somehow. Source: about 2 years ago
  • UI Design Roadmap 2023
    Step 5: Study design system Atlassian design system Primer design system Spectrum, Adobe’s design system Carbon design system. - Source: dev.to / over 2 years ago
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What are some alternatives?

When comparing Scikit-learn and Atlassian Design, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Design Principles - An open source repository of design principles and methods

OpenCV - OpenCV is the world's biggest computer vision library

Colorbox.io - Create accessible color systems 🎨

NumPy - NumPy is the fundamental package for scientific computing with Python

Facebook Design - Resources for Designers from the Facebook Design team

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