Altair RapidMiner is appreciated for its ease of use and the CRISP data mining model it supports, covering steps like data preparation, data understanding, and business understanding. The tool’s auto model feature is excellent as it allows simulation of models to select the best one. It is useful for predictive analytics with community support for model adjustments. I also find handling complex datasets promising, although there's a need for improvement with generative AI adaptation. View full review »

Altair RapidMiner is easy to use and intuitive with no coding required, making it a low code tool. It offers flexibility, allowing for extensive data preparation and initial insights, and facilitates building a chain of analysis. Additionally, it includes machine learning and AI tools to work on complex datasets. It is considered a stable product with a long-standing presence. View full review »

RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. It can also connect to databases, allowing me to build models directly on the data stored there. RapidMiner offers a wider range of operators than other tools like Dataiku, making it a better option for my needs.

I can install it on my machine and access various extensions from the RapidMiner marketplace. The user interface is also user-friendly, and there's ample documentation available. 

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One of the most valuable features is the built-in data tuning feature. Once the model is built, we often struggle to increase its accuracy, but RapidMiner allows us to fine-tune variables. For example, when working on a project, we can adjust the number of nodes or the depth of trees to see how accuracy changes. This flexibility lets us achieve higher accuracy compared to traditional automated machine-learning models.

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The solution is very intuitive and powerful. It is easy for a beginner to learn the product. There are many tutorials available on the channels. We can also find more information on the web.

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What I like about RapidMiner is its all-in-one nature, which allows me to prepare, extract, transform, and load data within the same tool. Its seamless integration with other tools and visualization platforms, coupled with its capability to create machine learning algorithms and predictive analyses, adds significant value by simplifying tasks and streamlining workflows.

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The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically.

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There are many great features available. 

It's a good solution for a beginner data scientist. It's easy to use. Even management that may not have technical skills would find it easy and practical. 

It is easy to set up.

The solution is stable. 

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It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive. 

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We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space. That is something we value and so do our customers in Portugal.

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The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS.

RapidMiner is easy for people to get started when they want to use it. They do not have to be very skilled and can easily put up a data flow, read data, remove the missing values, tidy up the data and then put it through a decision tree or regression analysis.

It is easy to add functionality using Python or R code.

Their tutorials, including the videos, are very good.

Another powerful feature is that it will try out a large variety of machine learning algorithms and then compare them. If you're a beginner, it's a good way of getting started because it will point you in the right direction. For example, it will point you towards decision trees or support vector machines, and it will tell you why.

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I've been using a lot of components from the Strategic Extension and Python Extension. These extensions have been very helpful in integrating RapidMiner with my solutions.

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Since the beginning, we had a really nice onboarding experience with RapidMiner.

The solution is fast when it comes to prototyping. The prep and auto model feature is something that people really like. They help you prototype a use case very quickly. The quick prototyping features that are included in the software get everything ready - including the model instructions and all that.

I do see RapidMiner as much more of a data science platform, and not really an art restoration platform. The data science, collaboration, and IDN are very, very strong.

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The best thing about RapidMiner is efficiency.

RapidMiner is very easy to use. You just have to connect operators to launch an analysis state. You read the data with one operator, then filter it with another, and clean it with another. Then, you use an operator for the machine learning algorithms and the results are generated. In contrast, when you are programming in Python you have to write all of the instructions and it takes longer to do.

In the most recent version, they added automation of the cleaning of data, which is a very interesting feature.

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The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model.

The features the solution offers are quite extensive. We haven't had a chance to utilize all of them yet. The solution is constantly evolving to continue to be cutting edge.

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The feature that I like the most is the business of utilization. You just have to connect with an operator to launch an analysis state to read the data. Then, you filter with another operator and clean it with another one. You can also use machine learning algorithms and then it gives you the results.

Compared to when you are programming with Python, where you have to write all of the instructions, this is better because the effort of programming takes more time.

The best part of RapidMiner is efficiency.

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The most valuable feature is what the product sets out to do, which is extracting information and data. 

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The most valuable features are the Binary classification and Auto Model.

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The documentation for this solution is very good, where each operator is explained with how to use it.

There is a very clear structure between building a process and then looking at the results.

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I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries.

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1. Availability of cutting-edge data science tools and algorithms
2. Ease of code-optional GUI
3. Open Source Java core
4. Easy integration with APIs, Python, R, cloud storage, cloud computing, etc.

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• No code is necessary, no R and Python skills. 
• Using the GUI, I can have models and algorithms drag and drop nodes.

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