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SigOpt VS MLlib

Compare SigOpt VS MLlib and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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SigOpt logo SigOpt

Optimize Everything. Tune your experiments automatically to get better results, faster. A/B testing.

MLlib logo MLlib

MLlib is Spark's machine learning (ML) library that make practical machine learning scalable & provides ML Algorithms.
  • SigOpt Landing page
    Landing page //
    2023-04-09
  • MLlib Landing page
    Landing page //
    2023-06-12

SigOpt videos

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MLlib videos

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Using Spark Mllib Models in a Production Training and Serving Platform Experiences and ExtensionsA

More videos:

  • Review - Spark MLlib
  • Review - Announcement: LIVE on 26th July [ Spark SQL & MLLib ]

Category Popularity

0-100% (relative to SigOpt and MLlib)

SigOpt

MLlib

Data Science And Machine Learning
62 62%
Data Science And Machine Learning
38% 38
Python Tools
63 63%
Python Tools
37% 37
Data Science Tools
62 62%
Data Science Tools
38% 38
Software Libraries
50 50%
Software Libraries
50% 50

User comments

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Social recommendations and mentions

Based on our record, MLlib seems to be more popular. It has been mentiond 2 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.

SigOpt mentions (0)

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

MLlib mentions (2)

  • Predicting Diabetes In Patients - Apache Spark Machine Learning - 4 Easy Steps To Do This!
    The MLlib library gives us a very wide range of available Machine Learning algorithms and additional tools for standardisation, tokenisation and many others (for more information visit the official website Apache Spark MLlib). (Apache Spark Machine Learning predicting diabetes in patients). Source: about 2 years ago
  • How to distribute ML tasks across CPU and GPU?
    Totally agree with the current responses, especially for the purposes of understanding exactly what's going on under the hood, but did want to just call out the fact that you can simply use a machine learning library that's implemented in a distributed way. Examples would be MLlib From Spark and h2o. H2O in particular will take care of pretty much everything for you in terms of initializing a cluster, and has a... Source: about 2 years ago

What are some alternatives?

When comparing SigOpt and MLlib, you can also consider the following products

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

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

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

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

Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

Exploratory - Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.

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