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Backendless VS Scikit-learn

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

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  3. » Reviews
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Backendless logo Backendless

Backendless is a mobile Backend as a Service (mBaaS) platform.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Backendless Landing page
    Landing page //
    2023-07-20
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Backendless features and specs

  • Codeless Development
    Backendless offers a 'Codeless' feature, which allows users to build backend logic without writing any code. This is particularly beneficial for those who are not familiar with complex coding languages.
  • Real-Time Database
    The platform provides real-time data synchronization, allowing applications to update data instantly across all clients. This is essential for interactive applications such as chat apps and real-time data feeds.
  • API Services
    Backendless allows the creation of REST and SOAP APIs effortlessly. This makes it easier to integrate with other services and provides a clear pathway for extending app functionality.
  • User Management
    The platform comes with built-in user management features such as registration, login, password recovery, and social logins. This helps in reducing the effort required to implement user authentication and authorization.
  • Mobile and Web App Support
    Backendless supports both mobile (iOS/Android) and web applications, offering SDKs for multiple platforms which streamlines the development process.

Possible disadvantages of Backendless

  • Pricing
    Although Backendless offers a free tier, many features and higher usage levels are locked behind a paywall. This may be prohibitive for startups or small projects with limited budgets.
  • Learning Curve
    Even though Backendless offers codeless development, mastering the platform as a whole can be challenging for beginners. There are many features and settings that require some time to understand fully.
  • Vendor Lock-In
    Relying too much on Backendless-specific features can create difficulties if you decide to migrate to another backend service in the future. The migration process can be complex and time-consuming.
  • Limited Customization
    While Backendless offers many out-of-the-box features, there can be limitations in terms of customizing the backend behavior in comparison to building a custom backend.
  • Community and Support
    The community around Backendless is smaller compared to more established backend solutions like Firebase. This can make finding community support, third-party plugins, or comprehensive tutorials harder.

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.

Backendless videos

+ Add

Backendless 5 Release Overview (webinar)

More videos:

  • Review - Functionality Visibility Control in Backendless Console
  • Review - Backendless version 3.0 Overview

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 Backendless and Scikit-learn)

Backendless

Scikit-learn

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Databases
100 100%
Databases
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 Backendless and Scikit-learn

Backendless Reviews

2023 Firebase Alternatives: Top 10 Open-Source & Free
There are three comprehensive plans of this BaaS vendor: Backendless Cloud, Pro and Managed. But it only opened the pricing details of Backendless Cloud in this regard. Here are the key components of Backendless Cloud pricing:
Source: digitalcruch.com
Firebase Alternatives – Top 10 Competitors
Backendless is a highly scalable mobile Backend-as-a-Service (mBaaS) platform providing gazillion of features, including user authentication, live audio and video streaming, message filtering, push notifications, auto-scalability, data persistence, file storage, geo-location, cloud-code, analytics, and custom business logic. It has it all what you need to build awesome...
Source: blog.back4app.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

Scikit-learn might be a bit more popular than Backendless. We know about 31 links to it since March 2021 and only 21 links to Backendless. 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.

Backendless mentions (21)

  • Ap Developer
    Go here: https://backendless.com/ . If that don't work for you, Let me know and I'll tell you what next to do. Source: about 2 years ago
  • Join the Free Database Training Course From Backendless
    This article first appeared on https://backendless.com. - Source: dev.to / over 2 years ago
  • free-for.dev
    Backendless.com — Mobile and Web Baas, with 1 GB file storage free, push notifications 50000/month, and 1000 data objects in table. - Source: dev.to / over 2 years ago
  • How Much Does Custom Software Development Cost?
    Luckily, instead of building the backend from scratch, some backend Application Programming Interfaces (APIs) are available. Consider the following options: REST API, Firebase, Backendless, and JHipster. Using APIs is a great way to adopt a functional backend with lower custom software development pricing. - Source: dev.to / almost 3 years ago
  • Urgent: Low code / No Code App Builders
    The best no-code/low-code platform for building both the frontend and backend in one place is Backendless. They have the best backend features and a really solid UI Builder that gives you pretty much all capabilities you'll likely need. Source: almost 3 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 Backendless and Scikit-learn, you can also consider the following products

Datomic - The fully transactional, cloud-ready, distributed database

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

MarkLogic Server - MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities.

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

Valentina Server - Valentina Server is 3 in 1: Valentina DB Server / SQLite Server / Report Server

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

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