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Qubole VS XGBoost

Compare Qubole VS XGBoost and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Qubole logo Qubole

Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

XGBoost logo XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
  • Qubole Landing page
    Landing page //
    2023-06-22
  • XGBoost Landing page
    Landing page //
    2023-07-30

Qubole videos

+ Add

Fast and Cost Effective Machine Learning Deployment with S3, Qubole, and Spark

More videos:

  • Review - Migrating Big Data to the Cloud: WANdisco, GigaOM and Qubole
  • Review - Democratizing Data with Qubole

XGBoost videos

+ Add

XGBoost Part 3: Mathematical Details

More videos:

  • Review - XGBoost A Scalable Tree Boosting System June 02, 2016
  • Review - Free Udemy Course - CatBoost vs XGBoost - Classification and Regression Modeling with Python

Category Popularity

0-100% (relative to Qubole and XGBoost)

Qubole

XGBoost

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Big Data
100 100%
Big Data
0% 0
Business & Commerce
0 0%
Business & Commerce
100% 100

User comments

Share your experience with using Qubole and XGBoost. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, XGBoost seems to be more popular. It has been mentiond 1 time 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.

Qubole mentions (0)

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

XGBoost mentions (1)

  • CS Internship Questions
    By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning... Source: about 2 years ago

What are some alternatives?

When comparing Qubole and XGBoost, you can also consider the following products

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Open Text Magellan - OpenText Magellan - the power of AI in a pre-wired platform that augments decision making and accelerates your business. Learn more.

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