RapidMiner Reviews

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.

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.

We primarily use the solution for training and deploying various supervised and unsupervised models in the area of financial crime management.

It enables banks to quickly try and experiment multiple algorithms on same data set without the worry to have full time data scientists working. Focus shifts to data procurement, feature engineering and model validation rather than to worry about coding the same in other languages.

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.

The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated BFSI environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team.

If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery.  However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator.

I've been using the solution for about 3 years.

The stability of the solution is good. We haven't faced any bugs or glitches. However, it does depend on the data model you are working on. So far, for example, we haven't modeled a big data site, so I'm not sure what stability would be affected in a case like that.

In terms of scaling, it depends on the model you are deploying. When you build a model in a batch, scale is not an issue, because you can run it for hours or days to train the model. Currently, our company works within banks where we process around 10-20,000,000 transactions per day on a single site in the bank. 

You need to balance everything. In a real-time system, like the way we are operating, where we have a high case of having to send a response in less than one second, we need to have a balance. There are various ways we make sure, according to the deployment model, that we can respond within that one-second timeframe.

 Typically the kinds of people using the solution are data scientists and data analysts.

In terms of technical support, whenever we face issues, the first place we go to are online forums or the solution's blogs. Typically, we can find an answer to our issues there. If there are issues that need to be fixed, they do offer extensions where you can write your own Python or R program to address them.

Before using this solution, we were mostly working on a native R and Python-based platform. It was more of an open-source tool. This is the first commercial tool we have used.

In terms of the initial setup, you don't need to install the solution because it's a desktop version. It's the server of the deployment model which is a bit more complex. However, a desktop version is a standalone application and it's pretty straightforward. 

Unless you have some preliminary understanding of how machine learning models work, you will not be able to use the tool. It's not just with RapidMiner, it's on any tool. You have to check the parameters for every algorithm and you need to understand how algorithms work. Even with the excellent GUI and auto model capabilities, you'll still need the have a decent level of data science or machine learning knowledge.

We have an in-house team of data engineers, data analysts, subject matter experts, data scientists and ML engineers who collaborate with bank's IT and business team to deliver the solution. This is handled by dedicated team working under Professional consulting group.

This is confidential as banks do not usually share this information. However, given the ML platform with auto model capability, I can say ROI would easily exceed at least 90%. This again depends on how many models are trained and deployed on a regular basis.

Within the company, we have about seven user licenses. When it comes to clients, they typically only have one license, which is more than enough for their use.

We did evaluate a few others like IBM SPSS. But Rapidminer is very user friendly and has a robust rating on various leading portal like KDnuggets. 

We're in the banking and finance space, so mostly our clients use the on-premises deployment model. As part of compliance, it's required that data should not go out of the bank's boundaries or firewall.

This solution is a great tool for users that are experimenting and is an alternative to doing the coding and everything themselves. It's perfect for those who want to focus more on data analysis rather than spending days coding everything. Users can go pretty far because of the solution's Auto ML capability which cuts down on coding. It allows for great productivity.

I'd rate the solution eight out of ten.

Predicting which customers were most likely to churn for subscription-based contracts. Both RapidMiner Studio and RapidMiner Server were used to build, model, and deploy to production.

RapidMiner has moved companies toward being more data-driven. Decisions are made based on analysis of data and rapid prototyping, instead of gut feel.

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.

The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade.

Overall, I would rate RapidMiner at eight of 10. I have been able to use it effectively for all required use cases.

The solution is used for market-based analysis. that said, things like scoring models, predictive models, forecasting, have not yet been implemented. However, they have a lot of traction on industry 4.0. We have not just yet gotten into that. 

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.

In the Mexican or Latin American market, it's kind of pricey. 

The pricing can be a bit high.

Some of the data science platforms offer much more flexibility. Of course, there's not the same software for visual license results. It's somehow rigid.

I'd like to have a module for analytics there. For example, the capability of keeping track of changes in every version would be helpful. It was very, very difficult to track. 

Even as a partner, it is difficult to keep up with whatever changes they have in mind. On the commercial side, it has been the same. However, since I started, every three months, they propose a different commercial scheme. It's one of the reasons that they got lower marks on the Gardner report.

The UI is not super intuitive. It might be nice if, on the first time a person uses the product, there was a wizard that could walk a person through everything. It's supposedly very intuitive, and yet, I don't know what to, I don't know where to click, honestly. They need to offer a better-guided experience for beginners.

While the company likely has five or so years of experience with the solution, I only have three. However, I have had a lot of time to work with it. I've only been at the company for three years, which is why there is a time difference.

The scalability is important. For example, it's very easy to quote the desktop software. They tell, for example, how much data science users are going to be working with on the machines, in terms of creating models. You need to know how many concurrent users there are going to be. For that, you will have to quote the RapidMiner server. It can get very complex quoting once you get into operationalizing your models. 

They don't fully help you figure things out in terms of big accounts and scenarios. We'd like to have someone really technical assist. That type of person would know what questions to ask.

In terms of implementation, I will say that, that if a user gets the desktop version without the server, it's very difficult for them to actually get value out of the product. RapidMinor has discovered that the churn on the desktop product is very high. If you're going to get the desktop version, no worries, however, you have to have a clear strategy on how do the outputs of your models are going to make an impact on the business because the impact is not clearly seen sometimes.

The solution is considered expensive, at least in the Latin American market. They do try their best to give discounts whenever it's possible. However, the overall price is something to be cautious about.

We are using the latest version of the solution right now.

In general, we've been happy with the solution. I'd rate it at a nine out of ten.