Software Alternatives, Accelerators & Startups
Register | Login

Scikit-learn VS Travis CI

Compare Scikit-learn VS Travis CI and see what are their differences

IsDown.app
Monitor all your cloud services with our status page aggregator. IT teams choose IsDown to monitor all their vendors and get alerts whenever an outage occurs. Integration with Datadog, PagerDuty, Slack, Microsoft Teams, and a lot more. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Scikit-learn logo Scikit-learn

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

Travis CI logo Travis CI

Simple, flexible, trustworthy CI/CD tools. Join hundreds of thousands who define tests and deployments in minutes, then scale up simply with parallel or multi-environment builds using Travis CI’s precision syntax—all with the developer in mind.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Travis CI Travis CI for Simple, Flexible, Trustworthy CI/CD Tools
    Travis CI for Simple, Flexible, Trustworthy CI/CD Tools //
    2024-10-22

Founded in Berlin, Germany, in 2011, Travis CI grew quickly and became a trusted name in CI/CD, gaining popularity among software developers and engineers starting their careers. In 2019, Travis CI became part of Idera, Inc., the parent company of global B2B software productivity brands whose solutions enable technical users to work faster and do more with less.

Today, developers at 300,000 organizations use Travis CI. We often hear about the pangs of nostalgia these folks feel when they use Travis CI, as it was one of the first tools they used at the beginning of their career journey. We are still much here, supporting those who have stuck with us along the way and remaining the best next destination on your CI/CD journey, whether you’re building your first pipelines or trying to bring some thrill back into work that’s become overloaded with AI and DevSecOps complexity.

Our Mission:

We deliver the simplest and most flexible CI/CD tool to developers eager for ownership of their code quality, transparency in how they problem-solve with peers, and pride in the results they create—one LOC at a time.

Our Promise:

We aim for nothing less than to guide every developer to the next phase of their CI/CD adventure—even if that means growing beyond our platform.

Scikit-learn

Website
scikit-learn.org
Pricing URL
-
$ Details
Release Date
-
Edit details

Travis CI

Website
travis-ci.com
Pricing URL
Official Travis CI Pricing
$ Details
paid Free Trial $13.75 / Monthly (Per Month, Per User)
Release Date
2011 January
Edit details

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.

Travis CI features and specs

  • Ease of Use
    Travis CI offers a very user-friendly interface and straightforward setup process, making it accessible even for those new to CI/CD.
  • Integration with GitHub
    Seamlessly integrates with GitHub, allowing for automatic builds and tests triggered on pull requests and commits.
  • Wide Range of Language Support
    Supports numerous programming languages out of the box, providing built-in configurations for many common languages such as Python, Ruby, JavaScript, and Java.
  • Extensive Documentation
    Offers comprehensive and well-organized documentation, which can help users troubleshoot and understand complex setups.
  • Build Matrix
    Run your unit and integration tests across any combination of environments for comprehensive automation and absolute quality guarantees on your way to production.

Possible disadvantages of Travis CI

  • Pricing for Private Repositories
    Can become expensive for private repositories and larger teams, especially compared to some competitors that offer more generous free tiers.
  • Performance Issues
    Users have reported occasional performance issues, including slower build times and longer wait periods for queued jobs.
  • Limited Advanced Features
    Might lack some advanced features and customizations that are available in other CI/CD platforms, making it less suitable for very complex workflows.
  • Concurrency Limits
    Has limitations on the number of concurrent builds that can run, which can slow down development cycles for larger projects with many contributors.
  • Complex Configuration for Large Projects
    Configuration can become cumbersome and complex for large projects with intricate dependencies and multiple build steps.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Travis CI videos

+ Add

Setting Up Your First Build

More videos:

  • Tutorial - CI/CD Core Concepts
  • Tutorial - How to Get Started with Travis CI in 0 to 5 Minutes

Category Popularity

0-100% (relative to Scikit-learn and Travis CI)

Scikit-learn

Travis CI

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Continuous Integration
0 0%
Continuous Integration
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
DevOps Tools
0 0%
DevOps Tools
100% 100

User comments

Share your experience with using Scikit-learn and Travis CI. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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

Travis CI Reviews

The Best Alternatives to Jenkins for Developers
Travis CI is another popular cloud-based CI/CD solution that integrates well with GitHub. Known for its simplicity and ease of setup, Travis CI is a great choice for open-source projects or teams that primarily work with GitHub repositories. Its configuration is based on a YAML file, making it easy to define and manage build workflows.
Source: morninglif.com
Top 10 Most Popular Jenkins Alternatives for DevOps in 2024
Travis CI is known for its simple setup, quick parallel builds, and support for multiple architectures, including popular enterprise options like IBM PowerPC and IBM Z. It’s claimed that pipelines require approximately 33% less configurable code than other CI/CD solutions, which helps make the platform more approachable. Use it instead of Jenkins when you want a fast...
Source: spacelift.io
10 Jenkins Alternatives in 2021 for Developers
You might find that Travis CI proudly promotes the fact that they have more than 900,000 open-source projects and 600,000 users on their platform with Travis CI. Automated deployment can be quickly established by following the tutorials and documentation that are currently available on their website.
Source: solutionsuggest.com
The Best Alternatives to Jenkins for Developers
Travis CI is a continuous integration and testing CI/CD tool. It is free of cost for open source projects and provides seamless integration with GitHub. It supports more than 20 languages, like Node.js, PHP, Python, etc. along with Docker.
Source: www.lambdatest.com
Continuous Integration. CircleCI vs Travis CI vs Jenkins
Travis CI is recommended for cases when you are working on the open-source projects, that should be tested in different environments.
Source: djangostars.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Travis CI. 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 / 3 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 / 11 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
View more

Travis CI mentions (6)

  • Front-end Guide
    We used Travis CI for our continuous integration (CI) pipeline. Travis is a highly popular CI on Github and its build matrix feature is useful for repositories which contain multiple projects like Grab's. We configured Travis to do the following:. - Source: dev.to / over 2 years ago
  • Flutter
    CI/CD for autobuild + autotests (Codemagic or Travis CI). Source: over 2 years ago
  • How To Build Your First CI/CD Pipeline With Travis CI?
    Step 2: Log on to Travis CI and sign up with your GitHub account used above. - Source: dev.to / over 2 years ago
  • What does a DevOps engineer actually do?
    Some other hosted CI products, such as CircleCI and Travis Cl, are completely hosted in the cloud. It is becoming more popular for small organizations to use hosted CI products, as they allow engineering teams to begin continuous integration as soon as possible. Source: almost 4 years ago
  • Hosting an Angular application on GitHub Pages using Travis CI
    1. Let's create the account. Access the site https://travis-ci.com/ and click on the button Sign up. - Source: dev.to / almost 4 years ago
View more

What are some alternatives?

When comparing Scikit-learn and Travis CI, 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.

Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

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

CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.

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

Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.

Loading...