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Wasabi Cloud Object Storage VS Scikit-learn

Compare Wasabi Cloud Object Storage VS Scikit-learn and see what are their differences

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Wasabi Cloud Object Storage logo Wasabi Cloud Object Storage

Storage made simple. Faster than Amazon's S3. Less expensive than Glacier.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Wasabi Cloud Object Storage Landing page
    Landing page //
    2025-04-16

Wasabi Hot Cloud Storage is a scalable, cloud-based object storage service for various applications. It allows storing any type of data in any format, offering high-performance, reliability, and security at a minimal cost. Ideal for individuals and organizations seeking affordable, dependable data storage, Wasabi provides a highly durable and fault-tolerant infrastructure, ensuring data is always accessible and protected. With features like immutable buckets, versioning, and encryption, Wasabi ensures data integrity and security, making it a trusted choice for businesses and individuals alike.

  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Wasabi Cloud Object Storage

Website
wasabi.com
Pricing URL
Official Wasabi Cloud Object Storage Pricing
$ Details
-
Release Date
2015 January
Startup details
Country
United States
State
Massachusetts
City
Boston
Founder(s)
David Friend
Employees
100 - 249
Edit details

Scikit-learn

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

Wasabi Cloud Object Storage features and specs

  • Cost-Effective
    Wasabi offers competitive pricing for cloud storage without any hidden fees, making it a cost-effective option for businesses of all sizes.
  • High Performance
    Wasabi provides high-speed data retrieval and upload, which is ideal for applications requiring fast access to stored data.
  • Simple Pricing Model
    There are no fees for egress or API requests, which simplifies budgeting and cost management.
  • Scalability
    Wasabi's storage solutions can scale to meet the needs of growing businesses, providing flexibility as data storage requirements increase.
  • Security
    Wasabi offers strong security features, including data encryption both in transit and at rest, ensuring the safety of stored data.
  • S3 Compatibility
    Wasabi's storage service is compatible with the Amazon S3 API, making it easier for users to integrate with existing tools and workflows.

Possible disadvantages of Wasabi Cloud Object Storage

  • Limited Services
    Compared to larger cloud providers like AWS or Google Cloud, Wasabi focuses primarily on storage and offers fewer ancillary services and features.
  • Geographical Availability
    Wasabi has fewer data center locations worldwide compared to major competitors, which might impact performance for users in certain regions.
  • Customer Support
    While Wasabi offers customer support, it may not be as comprehensive or as responsive as the support provided by larger cloud service providers.
  • Ecosystem Integration
    Although it supports S3 compatibility, Wasabi might not integrate as seamlessly with other cloud ecosystem services beyond storage.
  • No Free Tier
    Unlike some competitors, Wasabi does not offer a free tier for basic storage needs, which could be a drawback for small businesses or startups.

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.

Wasabi Cloud Object Storage videos

+ Add

Introduction to Wasabi Hot Cloud Storage

More videos:

  • Review - Introduction to Wasabi Hot Cloud Storage (August 2021) | Wasabi

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 Wasabi Cloud Object Storage and Scikit-learn)

Wasabi Cloud Object Storage

Scikit-learn

Cloud Storage
100 100%
Cloud Storage
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 Wasabi Cloud Object Storage and Scikit-learn

Wasabi Cloud Object Storage Reviews

7 Best Amazon S3 Alternatives & Competitors in 2024
Wasabi hot storage differentiates itself against industry giants like Amazon S3 by offering storage that’s six times faster and up to 80% cheaper.
Source: diggitymarketing.com
Wasabi, Storj, Backblaze et al, are promising 80%+ savings compared to Amazon S3... What's the catch?
On the surface, Wasabi looks appealing with a simple pricing structure ($5.99/TB/mo) that comes with free egress and free operations. How can Wasabi afford this, you ask? Well, their business model relies on their user-base keeping their data stored (and unchanged) for some time and not consuming more than their fair share of resources, which they regulate via a handful of...
Source: dev.to

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, Wasabi Cloud Object Storage should be more popular than Scikit-learn. It has been mentiond 70 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.

Wasabi Cloud Object Storage mentions (70)

  • Using ColdFusion to Generate Pre-Signed Wasabi Download URL
    There was an internal decision to use Wasabi Cloud Storage instead of Amazon S3 and I needed to use ColdFusion to generate a pre-signed URL to allow access to AI-generated content for a limited time. I had used the Sv4Util.cfc and aws-cfml libraries before with Amazon and thought it was just as simple, but I got confused somewhere along the way and it just wasn't working. - Source: dev.to / about 2 months ago
  • How much 1 TB of egress costs by cloud provider
    This table is missing Wasabi [0], which has free egress. [0]: https://wasabi.com. - Source: Hacker News / over 1 year ago
  • What makes backblaze better than some of the other options out there?
    Backblaze is great because it's a set price, unlimited, and I don't have to think twice about it. I use Arq to backup my machine + external drives (several drives with lots of photos) to my local NAS. Was sending data to Wasabi, but the costs got out of control. I can purchase a year's worth of Backblaze + the 1 year revision upgrade for much, much less of what I was paying at Wasabi. Source: almost 2 years ago
  • The NixOS Foundation’s Call to Action: S3 Costs Require Community Support
    What about looking at Wasabi? It’s $5.99 per TB per month https://wasabi.com. - Source: Hacker News / almost 2 years ago
  • A web application that will need to store lots of image files. The company wants to use Dropbox for image storage. Is this okay?
    No, use AWS S3 or https://wasabi.com/ if you are worried about cost. Source: almost 2 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 / 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
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What are some alternatives?

When comparing Wasabi Cloud Object Storage and Scikit-learn, you can also consider the following products

Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.

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

Contabo Object Storage - S3-compatible cloud object storage with unlimited, free transfer at a fraction of what others charge. Easy migration & predictable billing. Sign up now & save.

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

Hetzner Object Storage - Scalable object storage, S3-compatible and ideal for growing data volumes. Secure and flexible for efficient data storage.

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

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