RapidMiner Reviews

I use it for machine learning and big data.

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.

I would like to see more integration capabilities. 

I have been using this solution for three years. I use the latest version. 

It is a stable solution.

It is a scalable solution.

I reached out to support through either the support portal or community support. 

I used a small visualization tool. 

RapidMiner is a little bit difficult to setup. It is neither easy nor too difficult to set up.  

We pay for a license. 

Overall, I would rate the solution a seven out of ten. I would like to see more integration capabilities. 

RapidMiner is primarily used to develop and deploy machine learning workflows to real business use cases, e.g. predictive analytics, segmentation, etc...

As a data science consultant, I was able to custom-build a machine learning lead generation (web scraping) tool for a client and have it be operational in 72 hours.

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.

I would like to see wider adoption of the RapidMiner platform by the Open Source community as a viable alternative/companion to Python and R.

No.

No.

Superb.

I used to use Excel. It was horrid.

Not at all. Lots of YouTube videos and documentation. Super easy.

RapidMiner has a freemium pricing model so, as long as your dataset has fewer than 10,000 rows, you have free software. There is also free academic licensing for Higher Ed or researchers. For companies, RapidMiner's paid license is much cheaper than SAS, SPSS, or other competitors. You definitely get your money's worth.

SAS and SPSS.

Sign up and read what others do with RapidMiner on the user community (community.rapidminer.com).

We use RapidMiner to do data modeling and forecasting for things like bank loans. It helps us determine which bank loans have potential and which loans can be issued. So, for example, we use the data from our existing customers to make predictions about new borrowers. That way, we can estimate how well the loan will perform.

Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there. 

I've been using RapidMiner for around two years.

Right now, we're trying to see how stable the profiling and modeling features are, so we are still experimenting through trial and error.

I can't say much about scaling up RapidMiner because we're using it for only one department.

RapidMiner technical support offers so much information. I feel that they've been helpful. 

Installing RapidMiner is relatively straightforward. I am only on a license for analyzing the data by itself. After installation, you have to optimize the module applications using the standard process and methodology, so the total time for deployment is around two or three weeks.

We pay the RapidMiner license monthly.

I rate RapidMiner 10 out of 10. It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry.

RapidMiner is a strategic machine learning solution, so it covers all the use cases in the machine learning landscape, including clustering, churn prevention, and anything else that has to do with predictive modeling. 

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.

We've been using RapidMiner for a couple of years at least, maybe more.

RapidMiner is a stable solution with a long history in the machine learning segment. It's always in the best position within the Gartner Quadrant and Forrester and so on. It's a complete and stable tool that is growing a lot. Other competitors are developing as well, but it's just a matter of preference.

RapidMiner is for companies that want to start a long journey in machine learning. It's not for experimenting. I would not use RapidMiner for that because it's a comprehensive and trusted company solution. That's why it's always leading in the Gartner Quadrants.

It's a little difficult to assess what RapidMiner support is like on average. We are partners, so the support is incredible. However, I don't know if this is the case for people who aren't. From a partner's perspective, I would rate their support 10 out of 10. Whenever we need them, they are there. But I think technical support is one of the main advantages of RapidMiner.

Setting up RapidMiner is straightforward, and we are a partner, so the vendor provides us with prompt support when needed. We need two or three people for maintenance but no more than that. Usually, some technical guys are involved whenever you set up this kind of infrastructure. Still, you typically start with some specific use case in a project base, so a manager is involved along with business analysts and some data scientists. The IT architect works on the setup bits and pieces, and then they're not required anymore.

If you look at it from a long-term perspective, RapidMiner delivers value for the money over the long term, but the initial price is steep. Our experience shows that whenever clients start using it, they value it. They get benefits out of the platform and are happy with it, but the entry price is high.

RapidMiner isn't cheap. It's a complete solution, but it's costly. The pricing model is quite clear, but it's an expensive solution because it's powerful. When you have a powerful platform, you need to pay more. But this is based on feedback from our clients. It's not our view. We are a service provider, so we just listen and take notes.

I rate RapidMiner nine out of 10. If you're planning to get into machine learning projects, my advice is to start as fast as you can. Many of our clients waste too much time thinking about use cases. We advise our clients to start doing some modeling and not wait for the perfect data because the clients usually have some bottlenecks. They don't have complete and accurate data, so they think they can't have predictive modeling projects in place. Our advice is to hit the ground running and start retrieving some results.

RapidMiner interacts well with data. It is valuable, easy to use, and easy to look at, and that is awesome. 

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

In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner.

Maybe just go for a more improved interface. We're moving on to 2023, and we're supposed to move forward and keep improving our interface, in my opinion.

I have experience with this solution. I have used it for a couple of months. I am just a beginner and not an advanced user. Currently, I am doing some testing.

It is a stable solution. 

I've been testing it. If I want to know the price, I have to contact the salesperson. 

We are still in the process of choosing between products.

We evaluated SPSS and KNIME. 

As long as RapinMiner helps users interpret data and use it, there is no problem using it. 

Overall, I would rate the solution an eight out of ten. If the solution could improve its user-interface, that would make it a ten.

I used RapidMiner to help teach Data Science and Statistics. The students understand better the pipeline of a data science project compared with development with a language like python furthermore the user can integrate easily python if he likes to. 

This solution assists me with instructing students on how to pre-processing data and use various machine learning algorithms.

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.

I would like to see all users have access to all of the deep learning models, and that they can be used easily.

RapidMiner loads very slowly, which is something that should be improved.

I have been using this solution for at least ten years. I have been using it since the first version was released.

This is a stable solution, although it sometimes takes a long time to load.

Whenever I have a problem, I try to solve it myself.

One time when I had a problem with deep learning, I tried to get answers through the forums. I tried to chat through the application but I didn't get an answer.

I have used many different tools and have experience with a lot of different software, and I can say that RapidMiner is very easy to use.

The initial setup of RapidMinder is very easy.

I installed the software myself.

I have been using the educational version.

I have not worked with all of the features in RapidMiner. For example, I have not worked with all of the features for Big Data, and I have not used it with the cloud.

I would rate this solution an eight out of ten.

I am testing RapidMiner as I am beginning data science. I will work with RapidMiner and Azure Machine Learning Studio. RapidMiner for Windows is an excellent graphical tool for data science.

I am using RapidMiner to generate insights for predictive maintenance in the Oil and Gas Industry, mainly for the electrical submersible pump system, including surface and down hole assets.

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

Improve the online data services.

The primary use case of RapidMiner is for teaching. I select data and give it to the students to clean. They use several algorithms, they compact them, they evaluate them, then give the results to me.

RapidMiner is used in my organization just in education

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.

I have the deep learning models on my laptop but it doesn't work very well. I think that they should make deep learning models easier. They are using deep learning models today for image processing and language processing.

I am a professor of Data Science among other things. I use RapidMiner when I give a machine learning course  and that for the last six years. I started with the first version of RapidMiner and now use the latest version.

Very good 

Not used yet

I have never contacted technical support. Any time that I have had an issue, I have tried to solve it myself. 

One time when I had an issue with deep learning, 

I use several solutions 

The initial setup is very easy. The software is very easy to use.

I have been teaching for nine years and because I have the experience, it is easy.

I have worked with RapidMiner, but I have not yet explored all of the functionality of the software. As an example, the relation with big data and the relation with the Cloud. I have used the utilities quite a bit.

In the last model, they added automation cleaning for data preparation. It is very interesting.

I am a computer scientist and I received my Ph.D. 23 years ago. I am a researcher, and when I have a problem, I use it to research and to find a solution to much more difficult problems.

I would rate this solution an eight out of ten.