Altair RapidMiner Reviews

I am a university professor in engineering and I am using RapidMiner for teaching. I use all the utilities of the software to illustrate some of the courses that I provide in data mining. I use the software everywhere I need it.

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.

RapidMiner can improve deep learning by enhancing the features.

In the next release, the solution should enhance the features. I think it is a complete solution, but some enhancements would be beneficial.

I have been using RapidMiner for approximately 12 years.

RapidMiner is a stable solution.

The scalability of RapidMiner is good.

Over the past year, I had 63 student users using the solution.

I have not needed to use the support from RapidMiner. It is easy to use and my students do not need it.

I have previously used other solutions, such as KNIME, Weka, and Orange. I use whatever solution is best for the use case. They all have some advantages.

The initial setup of RapidMiner is simple. The solution does not take a similar time to others solutions. However, if you were to do a cloud deployment it could be very difficult.

RapidMiner was deployed in-house.

For the university, the cost of the solution is free for the students and teachers. 

For someone who does not want to code this is the best solution and I would recommend it.

I rate RapidMiner a nine out of ten.

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 just use it for my coursework in school in order to handle a recommendation system and to clean data to prepare it for analysis.

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. 

I do not have any notes for improvement. It's worked fine for the assignment I use it for.

It sometimes takes time to load. That issue may be based on my system configuration.

I'm not sure if we are able to use custom code from Python. If not, it should. 

The pricing may be able to be lowered.

If they could include video tutorials, people would find that quite helpful. 

I've used the solution for less than a year. I have not used it for a very long period of time just yet. 

It is stable. It can take time to load. However, there are no bugs or glitches. It doesn't crash or freeze. I'd rate the stability nine out of ten. 

I'm not sure of the level of scalability. I haven't used it for every task or extensively.

For now, I'm just using it for myself, for education purposes. 

I have also used another solution in the past. RapidMiner is more robust. 

The initial setup is very straightforward. I'd rate the ease of setup eight out of ten. It's pretty easy.

Deployment only takes a few minutes. You need to download an installation file. The time it takes depends on the speed of your computer. 

While I haven't exploited the solution too much, I have found it to be a bit expensive. I need to compare it to other similar tools to really speak to pricing. 

I'm a student and use the solution on-premises for education purposes. 

I'd advise other users to look at other options and compare the findings. 

It's very easy-to-use, intuitive data mining software. I'd rate it nine out of ten. 

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. 

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.

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.

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.

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.