RapidMiner Reviews & Product Details

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.

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.

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 verry 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.

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.

I used RapidMinner in a Data Mining class, it was an easy program to use and it was easy to find and understand every operator (since everyone had an explanation). In my class we learned the basics but I hope I can use the program even more, I hope to learn more about it. Review collected by and hosted on G2.com.

The only problem I found was how to connect the operators sometimes. It takes time to understand that but it`s not impossible. Review collected by and hosted on G2.com.

Free for academic, Rapidminer automodel, complete tutorial, rapidminer academy, free certification, easy to use and template for several cases Review collected by and hosted on G2.com.

need to extend every year for faculty member, slow program, UI for operator not good looking Review collected by and hosted on G2.com.

How it works for clustering, one of the favorite tools my students have when I teach data mining. Review collected by and hosted on G2.com.

Now days, it requieres so much power from the computer and takes its resources to play, my students have complained about it and I think is one of the reasons why Gartner sent Rapidminer down in the magic quadrant in 2020 Review collected by and hosted on G2.com.

I am using Rapidminer for the last 6 months. I liked Rapidminer very much, it shows all processes transparently from reading the data to the final output. All processes are very transparent, and easy to use. We can see where our data flows clearly. There is a added benefit that anyone can understand what is happening to our data. We can update our processes very easily using graphical user interface. Most of the commonly used processes are already available in the RapidMiner as drag and drop. It is as easy as dragging and dropping the required process, and connecting its input and the output. Any one with basic idea of data analysis can use it comfortably. Review collected by and hosted on G2.com.

When running some processes, the software takes too much system resources. The computer becomes too hot, and unable to run if the data is big or the process is too large. We cannot run other programs during that time. If we want to rerun the process, we have to start from the beginning. There is no provision of saving the intermediate data during processing. We have to save the output then read again. that is the only one method that we can do. If we want to change any processes all controls, or all statistics is not available for the user. Review collected by and hosted on G2.com.

Auto Machine Learning feature. Application of best practices and the possibility of changing to a simple process, when users can analyze all the operators that have been used. Review collected by and hosted on G2.com.

I think the documentation generated by RapidMiner could be improved in RapidMiner, as this action will help all developers (data scientist) and customers in the presentation of models. Review collected by and hosted on G2.com.

I think that the software has great support in terms of explanations given on how different algorithms work. This is very helpful. Review collected by and hosted on G2.com.

I think it is a bit complicated in terms of having to create and connect different boxes to enable a process to run. Other type of software have a more user friendly type of approach. Review collected by and hosted on G2.com.

It is very easy to learn and supports many ML algorithms. I like its process-based design and the fact that there are lots of useful extensions. Review collected by and hosted on G2.com.

I hope it covers more statistical analysis. Also, I would like to see more training videos. Review collected by and hosted on G2.com.

The training sections both in the tool and on the website are great. Clear even for people that do not have much data analytics experience Review collected by and hosted on G2.com.

Turbo prep function gives very little control over the outcome. An option to indicate what the purpose of the project is or which algorithm will be used Review collected by and hosted on G2.com.

I like how any process can be made easily presentable and find the part of the process is quite simple. Review collected by and hosted on G2.com.

A greater description of when to use certain advanced parameters in processes would be helpful in addition to only an explanation of what that parameter does. Review collected by and hosted on G2.com.

data preparation is very easy and you can clean and prepare your data very fast. Review collected by and hosted on G2.com.

Visualization tools are very strict. they should allow us to play around more. Basically, it is good for the first impression but I can make better graphs with Python. Review collected by and hosted on G2.com.

RAPIDMINER is a great tool now for the analysis and visualization of data, it has a diversity of characteristics that allow you to do a more effective job. It provides the marketing department to adapt and improve its performance and solve problems through cloning that you can perform transformations such as calculations, percentages or dates to reuse the analyzes that allows significant time savings. Review collected by and hosted on G2.com.

Our experience has been very good and with results that are increasing each time, but a function that should be added is the automatic programming to generate data, this would be a great step to improve performance and productivity within Rapidminer,the support is nice and complete, I'm glad it worked perfectly. Review collected by and hosted on G2.com.

I like the fact that there are a lot of helpful guides and communities that can answer lots of questions that could pop up about using and designing models on RapidMiner. Additionally, I like that using RapidMiner itself, once you get the hang of it, is very easy and easy to understand. Review collected by and hosted on G2.com.

When first using RapidMiner, if you don't have some kind of outside help, it's confusing about where and how to start for beginners. Also at times, when I tried to run a model it always gave me an error that my computer didn't have enough space yet when a friend of mine ran the exact same thing with the exact same computer, it seemed to work just fine for them. This was a little frustrating. Review collected by and hosted on G2.com.

The visual workflow design approach can save time by allowing users to focus on the process and result and less on programming and troubleshooting. Additionally, there are many beginners who have a strong conceptual understanding in their field but are starting to jump into the field of data science and machine learning. Rapidminer's visual approach allows a wide range of users to benefits from its tools. Review collected by and hosted on G2.com.

The availability of certain algorithms can be improved or updated. For example, for clustering algorithms, beside k-means, there are some variations such as k-means++, x-means, and k-medians that can be included for comparison especially when you are using Auto Model. Review collected by and hosted on G2.com.

From a company finding the majority of its customers in the industrial sector, one might expect a customer interaction similar to how other Tech Giants behave. Nothing distinguishes RapidMiner more than any other company or application I used over more the last decades.

As a clinician (anesthesiologist and emergency physician), once an absolute rookie in data science I tested several platforms able to solve my data related problems. And problems, I had many of them from extracting unstructured data from electronic health records (EHRs) to model deployment and monitoring of disease specific datasets from critical patients with stroke, sepsis, covid-19, etc.

I could never have accomplished these tasks without the close relationship I developed over the years with amazingly interesting and clever people developing for or working with RapidMiner technology. A few things I experienced during that journey:

No matter which sector you work in, listening to questions other data geeks are facing, reading the solutions proposed by experienced people in a certain subdomain always gives you a step forward to solve your own problem. I never noticed such a vivid community as the one I visit almost daily with almost 700K members and over 15K discussions and answers. As most other companies have only a customer service, RapidMiner always valued the direct feedback from any customer or platform user. This definitely is the reason RapidMiner is a very successful application as it keeps the finger on the pulse. The company always knows which direction customers wanted to get their platform to grow.

Besides the passive inflow of information from the customers, RapidMiner is also actively interacting with its customers. National and international meetings and webinars are organized to get the latest trends and insights in the developments the company is working and additionally provides lots of time and space to have customers present their accomplishments.

As many data science applications evolve in the direction of monolithic black boxes, RapidMiner used a completely different approach. As much as developing new technologies within their platform, they offered solutions to integrate existing code (R, Python, etc) within their processes which saved many hours of coding as several tools are available e.g. in Python and provide links to databases, websites, web services, feeds, social media, etc.

As a clinician and data scientist, I need to spend a lot of time explaining the steps I took between raw data and the final model I developed. This is so often overlooked by many companies and from my point of view a significant reason the AI/ML revolution could have positioned itself at a higher level in many fields of directly aiding citizens to become healthier and more efficient.

The most striking argument I have to stick to RapidMiner is the fact that I only need one application to get most of my work done from ETL to data visualization which makes the amount of coding required to achieve my goals to a minimum. RapidMiner evolved in such a way that people with almost no experience in data science can cluster and classify their data, make predictive models, deploy models and monitor any drift in a way large industrial processes are dealing with this.

Additionally, the way the user interface is built, any process can be built starting with tangible building blocks which can be understood by any audience which will at one moment in time require insights in which magics you have created.

Although RapidMiner has most of its customers positioned in different industries more than in research departments, RapidMiner always showed lots of interest and provided me with valuable advice.

It may be very subjective but the word RapidMiner family is how I see this community and company.

As life is an inflammatory pathway, generating interesting data, RapidMiner will always be one my everyday carry essentials.

Sven Van Poucke, MD, PhD

https://www.researchgate.net/profile/Sven_Van_Poucke/research Review collected by and hosted on G2.com.

I do not find any relevant downsides for this moment Review collected by and hosted on G2.com.