TrustRadius Insights for RapidMiner are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Intuitive User Interface: Several users have praised RapidMiner Studio for its intuitive user interface, which has made it easy for them to learn and navigate the software. The intuitive workflow paradigm has allowed users to quickly grasp the functionality of the software and perform tasks with ease.
Versatile Operators: Many reviewers have highlighted the versatility and power of RapidMiner Studio's operators. These operators are complete and powerful, especially in handling tasks such as data preprocessing, data visualization, and data mining analytics. Users have found these operators to be valuable tools in various areas of analysis.
Extensive Support System: Numerous users have commended RapidMiner Studio for its excellent documentation, countless worked examples, and large user community that provides training support. This extensive support system has been highly valued by users as it offers valuable resources for learning and troubleshooting, ensuring effective utilization of the software's capabilities.
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RapidMiner Reviews
3 Reviews
Engineering
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We are only 5 consultants. We are all using RapidMiner Studio in order to model machine learning structures for our clients. The problem we had before, for instance with Python code, is that it was really difficult for our clients to understand what we were doing exactly. Even when it is actually possible to build up visualization capacities in Python and R, RapidMiner gave us the option to do it so fast and accurately. I would love to see updates which can improve the graphical quality and precision, or prevent some graphical mistakes. For instance, it is very difficult to guess how much is too much when we have so many categories to diagram, sometimes saturates the space and reduces the explainability of the graphs. But still, AutoModel, Feature Engineering and the drag and drop method is wonderful to share our thoughts and discoveries about our clients reality and plans.
Aptus Data Labs is based out of Bangalore, India. We are a Big Data and Advanced Analytics company, providing consulting and project delivery services & solutions, catering to enterprises of all sizes, across different industries such as Healthcare, Retail & Consumer Industries, BFSI, Manufacturing & Supply Chain.
Since we are into advanced analytics, most of our solutions are delivered using RapidMiner. As a result of which, most of the employees in the organization use RapidMiner. We have dedicated developers for building extensions for RapidMiner as well. Some of the business problems built using RapidMiner are:
Fraud analysis for Banking and Financial industries
Claim and travel analytics for a manufacturing firm
Text mining and text analytics for a pharmaceutical firm and many other organizations
Optimization for e-commerce and manufacturing firm
Supply chain management for manufacturing
Supply chain planning and scheduling for oil and gas companies
Pros
A great tool to start exploring data science and machine learning. Its intuitive GUI, tutorials, help window, sample processes, and recommendations make it the best place to learn and expand your knowledge horizon.
RapidMiner is an expert in building end to end solutions. Creating a process in the studio and then running it in production using the server is easy and fast. And also using web services, we can integrate the solution into an organization's in-house application or create a new web application in RapidMiner server. This makes solution delivery faster compared to R and Python.
Text mining and analytics capability in RapidMiner. I think text processing is very easy here. Using Rosette and deep learning extensions, I have delivered such great solutions.
Smart Automations like automatically identifying parameter values, auto model and turbo prep etc. saves a lot of time and provide better results
Cons
RapidMiner Server- It is very basic in terms of appearance. Web Apps can be improved by providing default themes and it needs a lot more features to be added.
Multi-process window in RapidMiner Studio. Multiple design view can be added for switching between processes and model building can be made easier.
Git Integration for version control. We have something called MyExperiment in RapidMiner but it is far from Git. But if we could have git integration, multiple users can work on the same process and this version control can help to refer previous solutions as well.
Graphs in RapidMiner Studio are a bit old fashioned
Likelihood to Recommend
RapidMiner is the best tool to build models on textual data. It is rich in ML algorithms and reduces the need to manually tune the parameters. It automatically optimizes them, thus providing a better solution. RapidMiner again extends great capability for data preparation, its insane connections to almost every data source pulls in the data easily into one environment. And it can comfortably perform data cleaning and process tasks over that.
RapidMiner is not so good with image, audio or video data. These data points cannot be used directly in their raw form. They must be transformed into some intermediate form for performing analytics over it. Moreover, there are no connectors to directly pull data from their varied sources. For example, we don't have a connector to read audio data directly from a switch and then convert it to text (although Google speech API is available for audio to text conversion.)
We use RapidMiner Studio to build and test energy usage models for buildings.
Pros
Rapid prototyping of machine learning models.
Provides predefined parameters that are crowd sourced and provide helpful parameter ranges.
The interface is very easy to use, even for someone with no coding experience.
Provide model exportability.
Cons
We have had trouble exporting the models to languages like php.
The ability to build custom models would be useful, using scripting languages.
Ability to automate running the machine learning for multiple tasks would be useful.
Likelihood to Recommend
It's great to run preliminary tests on a dataset to identify which algorithms work best on it. It's not ideal for creating custom algorithms and models.
