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AWS Cloud9 VS Scikit-learn

Compare AWS Cloud9 VS Scikit-learn and see what are their differences

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AWS Cloud9 logo AWS Cloud9

AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • AWS Cloud9 Landing page
    Landing page //
    2023-04-23
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

AWS Cloud9 features and specs

  • Integrated Development Environment
    AWS Cloud9 provides a set of tools for coding, running, and debugging applications, making the development process more efficient.
  • Collaboration
    Real-time collaboration features enable multiple developers to work on the same project simultaneously, making teamwork easier.
  • Preconfigured Workspaces
    Preconfigured environments speed up the setup process, allowing developers to start coding immediately without worrying about configuration.
  • Serverless Development
    Supports serverless apps and provides seamless integration with AWS Lambda, helping developers build modern applications.
  • Remote Development
    Enables development from any location without the need for a powerful local machine, as the IDE runs in the cloud.
  • Cost Management
    Cloud9 uses pay-as-you-go pricing, potentially reducing costs compared to maintaining and upgrading local development environments.

Possible disadvantages of AWS Cloud9

  • Internet Dependency
    Requires an internet connection to access, which can be a limitation in areas with unstable or no internet access.
  • Resource Limitations
    Dependent on the allocated AWS resources, which may require scaling and can incur additional costs for high usage.
  • Latency Issues
    Potential latency issues could affect productivity, particularly when used over slower internet connections.
  • Learning Curve
    Users unfamiliar with cloud-based IDEs or the AWS ecosystem may require time to learn how to effectively use Cloud9.
  • Vendor Lock-In
    Being tightly integrated with AWS services, it may contribute to vendor lock-in, making it harder to switch to other cloud providers.
  • Cost Management Complexity
    The pay-as-you-go model can lead to unexpected costs if resource usage is not closely monitored and managed.

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 AWS Cloud9

Overall verdict

  • AWS Cloud9 is generally considered a good option for developers, especially those working within the AWS ecosystem. Its cloud-based nature allows for easy access from anywhere, and the environment simplifies the process of scaling applications. However, for developers not working with AWS services, or those who prefer offline development, it might not be the ideal choice.

Why this product is good

  • AWS Cloud9 is a cloud-based integrated development environment (IDE) that is particularly beneficial for developers who need a robust and flexible environment. It offers seamless integration with AWS services, making it easier to develop, test, and deploy applications in the cloud. Cloud9 supports a wide array of programming languages, provides tools for real-time collaboration, and includes features like code hinting, debugging, and the ability to work on serverless applications.

Recommended for

  • Developers who frequently use AWS services
  • Teams that require real-time collaboration on code
  • Developers who need a browser-based IDE
  • Those looking to leverage the power of serverless computing within AWS

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.

AWS Cloud9 videos

+ Add

Introducing AWS Cloud9 - AWS Online Tech Talks

More videos:

  • Review - Introduction to AWS Cloud9

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 AWS Cloud9 and Scikit-learn)

AWS Cloud9

Scikit-learn

IDE
100 100%
IDE
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Text Editors
100 100%
Text Editors
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 AWS Cloud9 and Scikit-learn

AWS Cloud9 Reviews

8 Best Replit Alternatives & Competitors in 2022 (Free & Paid) - Software Discover
AWS cloud9 is a cloud-based integrated development environment (Ide) That lets you write, run, and debug your code with just a browser. AWS cloud9 amazon web services.
Source: www.softwarediscover.com
Top 10 Visual Studio Alternatives
AWS Cloud9 is a cloud-based coordinated advancement system. It is a server that allows the users to type, initiate or operate and repair the code by only using the browser. It contains an editor program for code, error-removing system, and endpoint. Cloud9 has all the important tools for general programming languages, that includes,
Source: blog.back4app.com
12 Best Online IDE and Code Editors to Develop Web Applications
There are no additional charges for using Cloud9. You can connect Cloud9 to an existing/new AWS compute instance, and you pay only for that instance. It’s also possible to connect to a third-party server over SSH — for exactly no fee! 🙂
Source: geekflare.com
Ruby IDE: The 9 Best IDEs for Ruby on Rails Development
Here we are talking about a different animal all together – Cloud9. Cloud9 offers development environment for almost all programming languages including Ruby. Cloud9 is fast becoming popular among medium to large enterprises and companies like Heroku, Soundcloud, Mailchimp and Mozilla etc. are already using Cloud9.
Source: noeticforce.com

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

AWS Cloud9 might be a bit more popular than Scikit-learn. We know about 39 links to it since March 2021 and only 31 links to Scikit-learn. 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.

AWS Cloud9 mentions (39)

  • Serverless Data Processing on AWS : AWS Project
    AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser. It includes a code editor, debugger, and terminal. Cloud9 comes pre-packaged with essential tools for popular programming languages and the AWS Command Line Interface (CLI) pre-installed so you don’t need to install files or configure your laptop for this workshop. Your Cloud9... - Source: dev.to / 7 months ago
  • Codespaces but open-source, client-only, and unopinionated
    AWS has Cloud9[1] though it's worth pointing out that it's not an exact a 1:1 and may require some elbow grease to use in the same manner[2]. 1. https://aws.amazon.com/cloud9/ 2. https://aws.amazon.com/blogs/architecture/field-notes-use-aws-cloud9-to-power-your-visual-studio-code-ide/ (2021). - Source: Hacker News / almost 2 years ago
  • How does working with files through AWS work, do you save them onto the AWS console?
    If you just want to run an IDE for Python in the cloud, take a look at AWS Cloud9 (that would cost something however). You could get your code into AWS and sync your local changes using a source code repository, e.g. On GitHub or GitLab. Source: about 2 years ago
  • Best web-based IDEs?
    Not sure why you won't use replit but AWS has Cloud9 https://aws.amazon.com/cloud9/. Source: over 2 years ago
  • Taking my AWS CCP Exam today, any additional notes help, feel pretty good about the information I’ve reviewed, but please feel free to drop advice or notes.
    As I mentioned in a previous post, cloud9 was not in the course I was studying from, and not in the practice exams I solved. It came in my exam. Https://aws.amazon.com/cloud9/. Source: over 2 years ago
View more

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
View more

What are some alternatives?

When comparing AWS Cloud9 and Scikit-learn, you can also consider the following products

Codeanywhere - Codeanywhere is a complete toolset for web development. Enabling you to edit, collaborate and run your projects from any device.

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

Koding - A new way for developers to work.

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

Netbeans - NetBeans IDE 7.0. Develop desktop, mobile and web applications with Java, PHP, C/C++ and more. Runs on Windows, Linux, Mac OS X and Solaris. NetBeans IDE is open-source and free.

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

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