Altair RapidMiner Reviews

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.

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.

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.

Our primary use for this particular product is to provide it for a large medical research company. It is a research company that helps pharmaceutical manufacturers, distributors, and clinics with analysis. Our client collects data that they crunch to provide these types of companies information about trends in distribution and production within the pharmaceutical industry. They use RapidMiner to crunch that data.  

The results of the analysis of the data helps with pricing, it helps with determining volumes, it helps with projections, et cetera. The client has a global license for RapidMiner, so here in the Middle Eastern branch, they use the same product as they do globally.  

I think it is a fairly straightforward interface generally. It is an easy-to-use solution compared to SAS Enterprise Miner, for example.  

On the other hand, compared to some other products, maybe the UI could be enhanced. The visual interface could have something like the-drag-and-drop features which Alteryx already supports. Some of those additional features can make RapidMiner a better tool and maybe more competitive or advanced.  

We have been working with the product for maybe two years now.  

Stability is good with this solution.  

Scalability is not really a concern with RapidMiner. The implementation that I have seen of this product for our client now is very sizeable. It scales very well.  

I had previously used other solutions but not as a part of this company. It was a matter of who I was working for.  

The initial setup is something that is supposed to be straightforward according to the people who implemented the solution. I was not part of the setup directly, so I don't know how complicated that is hands-on and so I'm not sure exactly what is involved or the exact amount of time it might take. The initial data mounting might be a big task when it is done globally as it is for this client.  

The current licensing cost is advertised on the website. The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license.  

I used to work with a company where we sold a variety of products as system integrators. So I compared some product because we had to evaluate them to know the advantages and differences. At that time, we got to the stage where we were signing up with Alteryx as a partner.  

So I was in touch with those Alteryx guys and wanted to also compare their product with SAS Enterprise Miner, who we already partnered with, and with RapidMiner as well.  

A couple of customers were already using RapidMiner and we were in the process of partnering with Alteryx. We have to know the other products even if it is through secondary research in addition to what the vendors are presenting.  

On a scale from one to ten where one is the worst and ten is the best, I would rate RapidMiner as around a seven. I choose seven because of the UI things and other parts of the product that might be improved. RapidMiner is more of an enterprise product. Here, in this region, most people like a packaged solution like Alteryx which covers more. Alteryx is also more attractive to many users because it is cheaper and easier to use from the perspective of the user interface.  

With Alteryx or Tableau, for example, you can just pick up data sources and then start EDL (enterprise data lake). It takes more effort to bring the data on to the data mart for RapidMiner and other enterprise products in the traffic mining category. These enterprise solutions have an additional level of complexity and flexibility but not everyone even needs it.  

Most of my work these days is to encourage younger members of staff in the university, as well as businesses outside, to get involved in data mining or machine learning. My requirement is that the tools have to be functionally good and also economically effective. So, products such as RapidMiner are very useful.

The primary use case at the moment is looking at psychiatric cases to see who is likely to commit suicide and who is not.

The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS.

RapidMiner is easy for people to get started when they want to use it. They do not have to be very skilled and can easily put up a data flow, read data, remove the missing values, tidy up the data and then put it through a decision tree or regression analysis.

It is easy to add functionality using Python or R code.

Their tutorials, including the videos, are very good.

Another powerful feature is that it will try out a large variety of machine learning algorithms and then compare them. If you're a beginner, it's a good way of getting started because it will point you in the right direction. For example, it will point you towards decision trees or support vector machines, and it will tell you why.

When I started using RapidMiner, I found it difficult to get it to read the metadata. I wanted to use, for example, a pivot table, and it did not have the variable or the attribute names in it. There were no values. It took a long while to figure out how to do that, although it tends to do it automatically nowadays.

RapidMiner is not utterly intuitive for beginners. Sometimes people have trouble distinguishing between a file in their own file system and a repository entry, and they cannot find their data. This is an area where this solution could be improved.

It would be helpful to have some tutorials on communicating with Python. I found it a bit difficult at times to figure out which particular variable, or attribute, is going where in Python. It is probably a simple thing to do but I haven't mastered it yet. I'd like them to do a video on that. There are a large number of videos that are usually well-produced, but I don't think that they have one on that. Essentially, I would like to see how to communicate from RapidMiner to Python and from Python to RapidMiner.

One of the things I do a lot of is looking at questionnaires where people have used Likert-type scales. I don't recommend Likert-type scales, but if they're properly produced, which is a lot of hard work and it's not usually done, they're really powerful and you can do things like normalizing holes on the Likert scale. That's not the same as normalizing your data in RapidMiner. So, I would want to get results with these Likert scales, pass it through RapidMiner, do a normalization and pass back both the raw scores and the normalized scores and put in some rules, which will say if it's high on the raw score and on the normalized score and low on the standard deviation, then you can trust it.

I have been using RapidMiner for three years.

I have not had any particular problems with stability but I've not really put it to the test with any really big datasets yet. 

I have not yet been able to assess scalability. If my university acquires the server version then this is something that I will be able to test. As so far I have been using the desktop version, the question has not yet arisen.

I have not contacted technical support. I've only used the user forum.

I have also used KNIME and IBM SPSS Modeler (formerly Clementine).

I used IBM Modeler for a very big project that involved building a rule-based system for controlling sewage. We experienced the knowledge acquisition bottleneck during the interview process, as we interviewed experts and they could not tell us what the rules were. If we asked them what to do if the sewage became smelly and the water stopped flowing, they could not tell us a specific solution such as "If this chemical is a certain level and the temperature is above a certain value and the slope or the ground is more than a certain amount...", etc. Rather, they would give us anecdotal examples such as "Last week, we had this case where..." Using all of these examples, we were able to generate rules using machine learning.

We have also used VisiRule in several projects, which is an expert system building tool. We are thinking about using it again now, and I contacted them only a month ago. They can take a PMML file and turn it into an expert system. The question was whether they could take a dataset and create a PMML file, and with RapidMiner, I could.

The main competitor to RapidMiner is KNIME and it is much more cumbersome when you have to read SPSS files. You would first need to put in some R code in order to do that.

My first deployment was trivial because I used to use the Iris data set, which took me six or seven minutes to set up. With my current larger dataset, it has taken four or five weeks.

RapidMiner is adopting the same principle that SPSS has used, which makes it cheap or free at the university. When people graduate and go out to work in the industry, they are already familiar with the tool.

Although we don't pay licensing fees because it is being used within the university, my understanding is that the cost is between $5,000 and $10,000 USD per year.

I am also familiar with Weka and Alteryx, but I have not used them for real-world problems.

The trouble is that so many of these products now are almost identical in what they offer you. They learn from each other. So, they compete really on marginal advantages here and there such as cost, ease of use, and ease of learning.

I know businesses that have invested in RapidMiner, but I also know that many will go for KNIME because it is so cheap. It's free. It's more cumbersome to use, but after perhaps a week or ten days, you know it well enough.

Using RapidMiner is a two-stage process. At first, it's something simple whereby you can get quick results. This is done by clicking to get the mean standard deviations or the numerical variables, for example. You can get a bar chart and a frequency count of all of the categorical variables. I would suggest that you get somebody to do that, just to get used to it, but then stop them and make sure that they do a course on machine learning. Otherwise, they may be missing something important like cleaning up the data.

For example, I did one project many years ago whereby I was asked by the Department of Education in the UK to look at the survey data on primary school children. It had been done by a market research company and they were a bit uneasy about the results. They didn't know what was wrong with the results, but they felt they weren't right, so they asked me to look at it. The first thing I did was to take a simple look at the values of all the variables and the first thing that became clear was that on the bar chart of variables, the right-hand end shot up. This was the value 99, which was clearly a missing value.

Now it was a missing value, but in SPSS, which is what they use to finalize it, they had not declared it to be a missing value, so it found a child whose age is apparently 99 and they treated that child as being age 99. I found that out very easily by working out the arithmetic mean age of these primary school children, who should be under the age of 10, and their average age was 34.4. That came up merely because they hadn't specified a missing value.

Now that's a very simple example, but it's the sort of thing that can go wrong when people just use a package and they don't know what the underlying assumptions are. Or people produce a linear regression when the relationship is nowhere linear. I recently refused to referee an article, for example, from China, because they did linear regression on data which clearly were not linear. They were exponential in format.

All this to say that this is the two-stage process. You can get started very quickly, but you must then make sure that your staff is properly trained not to make these kinds of mistakes.

The beginning learning curve is very shallow, but when you want to go on and do really advanced things then it takes more time. Companies know this, so they try to find cheap solutions such as employing sociology graduates to use the software. They don't understand the issues the same way a computer science or mathematics graduate would.

With respect to functionality, at the moment it has more features than I need and can handle. 

I would rate this solution as 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. 

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.

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.