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Ingram Micro VS Scikit-learn

Compare Ingram Micro VS Scikit-learn and see what are their differences

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Ingram Micro logo Ingram Micro

Delivering global technology and supply chain services to support cloud aggregation, data center management, logistics, technology distribution, mobility device life-cycle and training.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Ingram Micro Landing page
    Landing page //
    2021-09-28
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Ingram Micro features and specs

  • Global Reach
    Ingram Micro has a vast global network, offering access to markets and customers around the world.
  • Comprehensive Product Portfolio
    The company offers a wide range of products and services, from hardware and software to cloud solutions, making it a one-stop-shop for many IT needs.
  • Strong Vendor Relationships
    Ingram Micro has established strong relationships with key vendors, ensuring reliable access to products and preferential pricing.
  • Advanced Logistics
    The company possesses sophisticated logistics capabilities, which help ensure timely and efficient delivery of products.
  • Financial Stability
    As a large, financially stable company, Ingram Micro can offer credit terms and financing options that smaller distributors might not.

Possible disadvantages of Ingram Micro

  • Complexity
    The sheer size and scope of Ingram Micro's operations can sometimes lead to complexity and inefficiencies.
  • Customer Service Issues
    Some customers have reported inconsistent customer service experiences, which can impact satisfaction and loyalty.
  • Higher Prices
    While Ingram Micro offers a wide range of products, their prices may be higher compared to smaller, more niche distributors.
  • Limited Customization
    Given their broad focus, Ingram Micro may not be able to provide the same level of customization and specialized services as smaller, more focused competitors.
  • Dependency on Vendors
    The company's success is closely tied to its vendor relationships. Any disruptions or changes in these relationships could impact product availability and pricing.

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.

Ingram Micro videos

+ Add

2019 Year in Review | Ingram Micro Commerce & Lifecycle Services

More videos:

  • Review - Trust X Alliance 2019 Year in Review | Ingram Micro
  • Review - Platinum Equity Buys HNA’s Ingram Micro for $7.2 Million

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 Ingram Micro and Scikit-learn)

Ingram Micro

Scikit-learn

CRM
100 100%
CRM
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
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 Ingram Micro and Scikit-learn

Ingram Micro 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 seems to be more popular. 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.

Ingram Micro mentions (0)

We have not tracked any mentions of Ingram Micro yet. Tracking of Ingram Micro recommendations started around Mar 2021.

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 Ingram Micro and Scikit-learn, you can also consider the following products

Cdw - cdw: ncurses interface for GNU/Linux command line CD/DVD tools

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

CompuCom - Technology Solutions for the Digital Workplace

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

Sirius - An open-source clone of Siri from UMICH

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

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