Software Alternatives, Accelerators & Startups
Register | Login

Scikit-learn VS Atlan

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

NYLE
The FedRAMP High gap analysis for teams who don't want a $150K consulting engagement. NYLE delivers a full NIST 800-53 Rev. 5 assessment in 7 daysโ€”with Moderate and Low included and 12 months of unlimited access to track your path to ATO. 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. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Scikit-learn logo Scikit-learn

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

Atlan logo Atlan

Atlan is an advanced data workspace developed to offer benefits to many different sources of data.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Atlan Landing page
    Landing page //
    2023-08-23

Scikit-learn

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

Atlan

Website
atlan.com
$ Details
-
Release Date
2018 January
Startup details
Country
Singapore
State
Central Region
City
Singapore
Founder(s)
Prukalpa Sankar
Employees
50 - 99
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.

Atlan features and specs

  • Collaboration
    Atlan provides a collaborative platform for data teams, allowing users to manage and share data assets, which can enhance teamwork and productivity.
  • Integration
    The tool integrates with a variety of data sources and services, enabling users to easily connect and manage diverse data assets in one place.
  • User-Friendly Interface
    Atlan features an intuitive and user-friendly interface, making it accessible to users of varying technical skills, from data engineers to business analysts.
  • Data Governance
    Atlan offers robust data governance features, such as lineage and metadata management, which help organizations maintain data quality and compliance.
  • Automation
    The platform allows for automation of repetitive tasks, which can save time and reduce errors in data management processes.

Possible disadvantages of Atlan

  • Complexity for Small Teams
    While feature-rich, Atlan might be too complex for smaller teams or projects that do not require comprehensive data management capabilities.
  • Cost
    Atlan's pricing may be a concern for smaller organizations or startups, as advanced features can come at a significant cost.
  • Customization Limitations
    Some users might find the customization options limited compared to other data management platforms, which could impact specific use-case implementations.
  • Learning Curve
    New users may experience a steep learning curve when starting with Atlan due to the extensive range of features and capabilities it offers.

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.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Atlan videos

+ Add

3-Minute Atlan Demo

More videos:

  • Review - ATLAN SAFE OUTDOORS Tent Chair Blind Review Vs. AMERISTEP Tent Chair Blind (WHATS BETTER??)
  • Review - Atlan: Leveraging Founder-market Fit to Build a Global SaaS Brand

Category Popularity

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

Scikit-learn

Atlan

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Business & Commerce
0 0%
Business & Commerce
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Office & Productivity
0 0%
Office & Productivity
100% 100

User comments

Share your experience with using Scikit-learn and Atlan. 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 Atlan

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

Atlan Reviews

We have no reviews of Atlan yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Atlan. While we know about 40 links to Scikit-learn, we've tracked only 2 mentions of Atlan. 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 (40)

  • Detecting Ingress Tool Transfer (T1105) with Python
    Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 1 month ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / about 2 months ago
  • Where to Get Hands-On AI Training for Cybersecurity Professionals
    Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / about 2 months ago
  • How Anomaly Detection Actually Works in Security Operations
    Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
  • Building a Personalized Meal Recommendation System
    In practice, youโ€™ll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
View more

Atlan mentions (2)

  • Thoughts around decube.io (data observability and catalog platform)
    After evaluating few solutions in the market: We were in the market to hunt for a solution which will cost under 10k (yearly) considering the cost of opensource will be similar considering DE resource and maintenance cost etc 1. MonteCarlo - Super duper expensive - Unable to hosting in Google Cloud 2. BigEye - Good features 3. Metaplane - Overall good package but when compared to catalog and other features it... Source: over 3 years ago
  • Data lake observability
    I've previously built data lakes on AWS with Glue and you get the data catalog for free but it isn't convenient to explore. Enterprise-grade data catalogs such as Alation are full featured and really decent but come at a higher cost. If your preference is open source, check out Atlan and Amundsen. Source: over 3 years ago

What are some alternatives?

When comparing Scikit-learn and Atlan, 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.

Minitab Connect - Minitab Connect is a data management platform that comes with cloud-based data and integration workflows having data governance and integration tools.

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

Microsoft Azure Purview - Microsoft Azure Purview is a unified data governance solution that provides capabilities that cover the entire lifecycle from ingestion to cleansing, transformation, and security.

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

Kylo - Kylo is an end-to-end data lake management software that provides data from many sources in an automated fashion and optimizes it.

Loading...