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Harness VS Scikit-learn

Compare Harness VS Scikit-learn and see what are their differences

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Harness logo Harness

Automated Tests For Your Web App

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Harness Landing page
    Landing page //
    2023-07-24
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Harness

Website
harness.io
Pricing URL
Official Harness Pricing
$ Details
-
Release Date
2016 January
Startup details
Country
United States
State
California
City
San Francisco
Founder(s)
Jyoti Bansal
Employees
250 - 499
Edit details

Scikit-learn

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

Harness features and specs

  • Continuous Delivery Automation
    Harness provides robust continuous delivery automation, allowing teams to automate deployment processes, reduce errors, and improve the speed of releasing software.
  • Finance Efficiency
    Harness includes a cost management feature that helps organizations control cloud infrastructure costs by providing insights and optimizing resource usage.
  • Simplified CI/CD Pipelines
    It offers simplified CI/CD pipeline creation with an easy-to-use interface, which reduces the complexity and time required to set up these processes.
  • Advanced Security Features
    Integrates advanced security features, such as vulnerability scanning and role-based access control, ensuring that deployments meet security compliance requirements.
  • Smart Rollbacks
    Harness has the capability of smart rollbacks that enable teams to automatically revert to a stable version if a new deployment faces issues, minimizing downtime.

Possible disadvantages of Harness

  • Learning Curve
    There can be a significant learning curve for teams new to Harness, as understanding its full capabilities and integrating it into existing workflows can take time.
  • Cost
    Harness might represent a higher cost than some other CI/CD tools, which can be a concern for smaller businesses or teams with tighter budgets.
  • Complex Configurations
    Some users report that setting up complex configurations can require advanced technical expertise, potentially necessitating additional training or hiring.
  • Integration Limitations
    Although Harness integrates with various tools, there can be limitations or complexities in configuring and maintaining these integrations, especially with less common tools.
  • Dependence on Internet Connectivity
    As a cloud-based solution, Harness requires reliable internet connectivity, which might pose challenges for teams with unstable internet connections or strict data residency requirements.

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.

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.

Harness videos

+ Add

How to FIT & CHOOSE a CLIMBING HARNESS w/ Black Diamond! VLOG

More videos:

  • Review - Best Climbing Harnesses - Top 7 Climbing Harness Reviews
  • Review - Best Dog Harness in 2019 - Top 5 Dog Harness Review

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Category Popularity

0-100% (relative to Harness and Scikit-learn)

Harness

Scikit-learn

DevOps Tools
100 100%
DevOps Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Cloud Computing
100 100%
Cloud Computing
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Harness and Scikit-learn

Harness Reviews

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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

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Harness. 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.

Harness mentions (15)

  • DevOps in 2025: the future is automated, git-ified, and kinda scary but fun.
    Harness — AI-powered delivery pipelines. - Source: dev.to / about 1 month ago
  • Best CI/CD for AWS services?
    Can check out our products at harness.io. Source: almost 2 years ago
  • How we moved from Artifactory and saved $200k p.a. Part 3 of 5 - The future is Advanced Artefacts
    Harness is our Continuous Delivery (CD) tool of choice. It provides a flexible template engine, that we were able to utilise to create templates that could be reused across our teams. - Source: dev.to / over 2 years ago
  • How to Install Drone CI Server in Kubernetes
    Drone by Harness is a continuous integration service that enables you to conveniently set up projects to automatically build, test, and deploy as you make changes to your code. Drone integrates seamlessly with Github, Bitbucket and Google Code as well as third party services such as Heroku, Dotcloud, Google AppEngine and more. - Source: dev.to / almost 3 years ago
  • Harness
    Does anyone have any opinion about the DevOps company Harness - harness.io? (they also have a defunct sub r/Harnessio/). How is the pay in India (Glassdoor and AmbitionBox gives very different figures). How is the work-life balance? In Glassdoor, it doesn't look good at all. If you are a current or ex-employee, would you advise rather to not join it? Source: almost 3 years ago
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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 / 6 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 / about 1 year 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 / over 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 / about 2 years ago
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What are some alternatives?

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

Deployment.io - Deployment.io makes it super easy for startups and agile engineering teams to automate application deployments on AWS cloud.

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

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

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

GitHub Actions - Automate your workflow from idea to production

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

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