Software Alternatives & Reviews

Adobe Sensei VS RapidMiner

Compare Adobe Sensei VS RapidMiner and see what are their differences

Adobe Sensei logo Adobe Sensei

Adobe Sensei harnesses data assets from high-resolution images to customer clicks all within a unified AI and machine learning framework.

RapidMiner logo RapidMiner

RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
  • Adobe Sensei Landing page
    Landing page //
    2021-10-09
  • RapidMiner Landing page
    Landing page //
    2022-06-12

Adobe Sensei videos

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RapidMiner videos

RapidMiner Review - Predictive analytics software review

More videos:

  • Review - Analyzing Customer Reviews with MonkeyLearn and RapidMiner
  • Review - SENTIMENT ANALYSIS OF MOVIE REVIEW USING RAPIDMINER FROM EXCEL FILE

Category Popularity

0-100% (relative to Adobe Sensei and RapidMiner)
Python Tools
7 7%
93% 93
Data Science And Machine Learning
Data Science Tools
6 6%
94% 94
Software Libraries
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Adobe Sensei and RapidMiner

Adobe Sensei Reviews

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RapidMiner Reviews

The 16 Best Data Science and Machine Learning Platforms for 2021
Description: RapidMiner offers a data science platform that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists...

Social recommendations and mentions

Based on our record, RapidMiner seems to be more popular. It has been mentiond 3 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.

Adobe Sensei mentions (0)

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

RapidMiner mentions (3)

  • I need help lol
    RapidMiner: A data science platform that offers an automated EDA process, including data preprocessing, visualization, and analysis. Source: about 1 year ago
  • Intro to Py-Arrow
    I hope this blog empowers you to start digging deeper into Apache Arrow and helps you to understand why we decided to invest in the future of Apache Arrow and its child products. I also hope it gives you the foundations to start exploring how you can build your own analytics applications from this framework. InfluxDB’s new storage engine emphasizes its commitment to the greater ecosystem. For instance, allowing... - Source: dev.to / about 1 year ago
  • Data Science toolset summary from 2021
    Rapidminer - RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. Link - https://rapidminer.com/. - Source: dev.to / over 2 years ago

What are some alternatives?

When comparing Adobe Sensei and RapidMiner, you can also consider the following products

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

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

Turi GraphLab Create - GraphLab Create is an extensible machine learning framework that enables developers and data scientists to easily build and deploy apps.

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

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