Implementing ML algorithms is a difficult business. Rapid Miner makes it easy to load your data, check your data for missing values, erroneous values, and correct them - and to implement both simple and advanced algorithms to understand your data better and to draw insights. Review collected by and hosted on G2.com.

There are lots of learning materials - RM Academy, video tutorials, etc, but they tend to the theoretical and make it quite difficult to implement one's exercise. The documentation within the program is really abstract and jargon-filled. I struggle when I read the functional labels and help explanations. Some of this is my own (beginner) level of expertise, of course, but I do think things could be spelled out more clearly and logically. Review collected by and hosted on G2.com.

Rapid Miner is a tool that is extremely easy to use. I especially appreciate its file handling capabilities and the immediate visibility of the data file I am working with. Even when I am tired or sick I can easily work with this program because of its drag-and-drop formula. There are many additional libraries that can be added if they are needed and it offers a wide range of available algorithms. I also like a lot of videos on You Tube which solved many of my problems. There are even courses on Coursera which teach data mining with RapidMiner. Review collected by and hosted on G2.com.

I had problems with the Operator library - after installation, it worked for a while and then stopped and informed me that it is not compatible with the main program. However, after re-installation it works again. Of course, Rapidminer has some limitations compared to Python or own programs, but I love it for its simplicity. Review collected by and hosted on G2.com.

You can save the k-means clusters models and use them again to find out insights with several data. Review collected by and hosted on G2.com.

First, until the year 2015, Rapidminer was collaborative, but now it is licensed software. Review collected by and hosted on G2.com.

Great tutorials and excellent explanations. Easy to use data sets for learning. Excellent platform for those new to AI and for experienced pros is still very useful and easy to use. Very intuitive interface. Excellent product. Highly recommend. Review collected by and hosted on G2.com.

Some areas not completely explained how certain algos work or integrate with data. Review collected by and hosted on G2.com.

We use rapidminer to quickly develop models and test them in practice. One of the best aspects imo is the rapidminer ai hub. You have great control over over the deployed models and a very sophisticated interface where you can monitor the performance of your model and quickly switch to another model if necessary. Also a nice way to avoid long complication are the integrated cost matrices. Personally i love working with rapidminer. Review collected by and hosted on G2.com.

There isn't much i dislike. The only thing that comes to mind is the python integration. Which can be a bit hard to debug at times. Review collected by and hosted on G2.com.

Numerous powerful algorithms are available. The novice-friendly UI. Options available of each operator. Easy to find the operator that I need. The software checks the operators for me. Review collected by and hosted on G2.com.

The UI could look more modern. The tutorial and documentation resources could be presented in a more organized way. Review collected by and hosted on G2.com.

It is very intuitive especially to non-coders like myself. They also provided educational licenses for academic institutions which is a big help to further academic use of predictive data analytics and to help foster advances in the academic fields. The RapidMiner community is also very active and helpful. The marketplace also provides useful and timely updates and add-ons which benefit a wide range of needs. Review collected by and hosted on G2.com.

There is a slightly steep learning curve for installation and first use. Manuals are also not updated, and newer learning materials (e.g. tutorial videos) should also be updated more regularly. Review collected by and hosted on G2.com.

Me and the students totally enjoy the ease of creating ML processes and the ability to quickly read in data, easily create processes and instantly have a look at the results. The extension mechanism gives access to useful extensions such as for building RNNs or for NLP. Everything is well explained and there is almost nothing you can't do even with very little ML background. Intuitive, Self-Explanatory, Powerful, Useful for ML Explorations, Rapid Prototyping Review collected by and hosted on G2.com.

Nothing comes to my mind. I guess if one would want to have more performant executions locally, this tool may not be the fastest option. It builds on top of Deeplearning4j, which has an option to have computations executed using GPUs, but I didn't see this option being propagated to the RapidMiner user. Review collected by and hosted on G2.com.

No requirement that students learn even the basics of a programming language and yet with RapidMiner they can estimate and evaluate machine learning models quickly Review collected by and hosted on G2.com.

Other than typos that I find in some of the explanations in the tutorials, RapidMiner has exceeded my expectations to date. thank you! Review collected by and hosted on G2.com.

RapidMiner is helpful and supportive of data science/analytics life cycle for any data sets. The operators and their list are exhaustive and can be connected per the required design. The tool handles all possible machine learning models right from data cleaning to data preparation to modeling to deploying the models. As long as the person handling the models using RapidMiner know the process of machine learning activities, how they need to be interconnected, use of various algorithms and their usefulness and limitations the toll provide a great deal of routine work pretty easily, and is very handy for all data scientists and analytics professionals along with business professional. Review collected by and hosted on G2.com.

I find some odds using this tool: AutoML does not seem to handle data from Turbo Prep unless I am missing something here. Also, AuotML process hook ups or connectivity seem to be complicated unnecessarily, the same results are achieved by using simpler operators’ connections. I worked on a problem where I have two data sets one for training with labels, and the other scoring data set where there is no target variable or target variable need to be predicted. In this scenario I was not able to use AutoML, which only can be used for training the data set. Probably I may be missing something! -Ramesh Paramkusham, Ph.D Review collected by and hosted on G2.com.