RapidMiner Tutorial - Introduction To RapidMiner

In this RapidMiner tutorial, we will start from the basics of RapidMiner and learn all the major RapidMiner concepts. Now, let’s have a look at the following concepts of this tutorial.

RapidMiner Tutorial for Beginners

In This RapidMinerTutorial, You Will Learn

What is RapidMiner  RapidMiner Products RapidMiner Studio RapidMiner Auto Model RapidMiner Turbo Prep RapidMiner Go RapidMiner Server RapidMiner Radoop

What is RapidMiner

RapidMiner is an integrated enterprise artificial intelligence framework that offers AI solutions to positively impact businesses. It is used as a data science software platform for data extraction, data mining, deep learning, machine learning, and predictive analytics. RapidMiner offers a free trial so that users can assess its capabilities. It is widely used in a number of business and commercial applications as well as in various other fields such as research, training, education, rapid prototyping, and application development. All major machine learning processes such as data preparation, model validation, results in visualization, and optimization can be carried out by using RapidMiner.

RapidMiner Products

RapidMiner is an integrated approach of the entire data science lifecycle from data mining to machine learning and predictive modeling. There are many products of RapidMiner that are used to perform multiple operations. Some of the products are

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RapidMiner Studio

It is a visual data science model that is used to design the workflows for validation of models accelerating the prototyping. With RapidMiner Studio, one can access, load, and analyze both traditional structured data and unstructured data like text, images, and media. It can also extract information from these types of data and transform unstructured data into structured.

RapidMiner Studio can blend structured data with unstructured data and then leverage all the data for predictive analysis. Its unparalleled set of modeling capabilities and machine learning algorithms for supervised and unsupervised learning are flexible, robust and allow it to focus on building the best possible models for any use case.

RapidMiner Studio provides the means to accurately and appropriately estimate model performance. The software has a strictly modular approach that does not let the information which is used in pre-processing steps leak from model training into the built-in application of the model. RapidMiner Studio makes the application of models easy, whether you are scoring them in the RapidMiner platform or using the resulting models in other applications.

The software also supports a variety of scripting languages, covering the not so easy data science use cases without using any software program. Apart from providing the various data and model building functionalities, RapidMiner Studio has a set of utility-like process control operations that lets you build processes that act like programs to perform loop tasks, call on system resources and branch flows. 

RapidMiner Auto Model

Auto Model is an advanced version of RapidMiner Studio that increments the process of building and validating data models. You can customize the processes and can put them in production based on your needs. Majorly three kinds of problems can be resolved with Auto Model namely prediction, clustering, and outliers.

With Prediction, classification and regression issues can be resolved. The auto model provides an evaluation of data, offers relevant models for problem-solving and once the calculations are completed, it compares the results of these models. Auto Model not just helps in generating accurate results but also helps you to analyze the results that are generated for deep learning models in which the internal logic is quite tough to understand. Auto Model can be seen as a view in Rapidminer Studio, next to the Results view, Design view, and Turbo Prep.

RapidMiner Turbo Prep

Data preparation is time-consuming and RapidMiner Turbo Prep is designed to make the preparation of data much easier. It provides a user interface where your data is always visible front and center, where you can make changes step-by-step and instantly see the results, with a wide range of supporting functions to prepare the data for model-building or presentation.

In order to not do the same job twice, Turbo Prep builds a RapidMiner process in the background. It is important to have consistent and useful data for preparing data models. Turbo Prep ensures to assemble every piece of important data together, eliminates worthless data, transforms the remaining data into a consistent and useful format, and presents the result.

Once you're done preparing the data, you can take additional actions like:

Model: Pass your data to Auto Model to help you build a model!

Charts: Display your data using a variety of charts.

Process: Save data preparation steps for use later as a RapidMiner process.

History: Look back at the history of data preparation, come back to a previous step, and make desirable changes.

Export: Save your data to a file, or save it in a RapidMiner repository.

RapidMiner Go

RapidMiner Go is an AutoML built for anyone - domain experts, business users, and analysts to make data science more accessible. Easily explore your data and assess the potential for machine learning to help solve a new problem. The software helps you to assess the data which is required and data models that are necessary for driving the impactful insights.

You can now deliver a machine learning model & full business case in minutes, Optimize your model for profits & ROI and make the whole analytics team more productive. RapidMiner Go helps you to understand different model types through a series of charts and visualizations and easily get your models into production.

RapidMiner Server

RapidMiner Server is a performance-optimized application server where you can schedule and run analytic processes and quickly return your results. It seamlessly integrates with RapidMiner Studio and other enterprise data sources to regularly update the processes so that they can reflect the changes to external data sources. In RapidMiner server, version management and shared repositories help in collaborating, creating interactive apps, and visualizing results locally or remotely using HTML5 charts and maps.

Main components to a RapidMiner Server configuration include:

1. RapidMiner Studio
2. RapidMiner Server
3. RapidMiner Job Agent
4. RapidMiner Job Container
5. RapidMiner Server repository
6. Data sources
7. Operations database

RapidMiner Radoop

RapidMiner Radoop is designed to eliminate the complexity of data science on Hadoop and Spark. Now, it is very easy to code Machine Learning for Hadoop & Spark, create predictive models with the help of RapidMiner Studio visual workflow designer. Also, you can make and execute predictive models in Hadoop without any need to code in Spark. RapidMiner SparkRM is meant to run data process flows in RapidMiner Studio parallelly inside Hadoop.

Radoop helps to maximize your investment in the Hadoop ecosystem by:

• Re-using existing SparkR, PySpark, Pig, and HiveQL code.
• Reducing risk and enforcing regulatory compliance with built-in Apache Sentry and Apache Ranger support.
• Deploying HDFS encryption to comply with data security policies.

Conclusion

RapidMiner’s products and features are a boom in data science that provides powerful capabilities for the users with a user-friendly interface that allows users to perform productively while working with data from the scratch. Thus, each of the tools’ robust components is easy to operate. The users get the set of tools that can make use of even the irrelevant, disorganized, and useless data by creating workflow and data models. This can be accomplished by enabling the users and their team to structure data in an easy way for them to comprehend. To perform the functions related to data science, RapidMiner offers products that can be used to simplify data access and its management so that it becomes easy for the users to upload, evaluate and access all data such as texts and images. Processed output can then be used to make sensible decisions that best suits you and your organization.