Streamlining Your Data Workflows with Altair RapidMiner

The terms data analytics, machine learning, deep learning and artificial intelligence are used quite commonly these days, so much that it’s becoming difficult to keep the definitions of these words straight. The goal of this post is to not dive too deep into defining these terms, but rather to look at how Altair RapidMiner can actually impact the workflow of a user.

Altair RapidMiner is an advanced analytics platform known for its ability to simplify data preparation, model building, and deployment. From data preparation and predictive analytics to anomaly detection and beyond, the intuitive interface, combined with a powerful backend library of algorithms and machine learning models, makes it a preferred choice among many data analysts. In this post, we will look at some of the features that can speed up or modify your workflows when handling data.

Streamlining Data Preparation

1. Automated Data Cleaning:

One of the most time-consuming aspects of handling data in large amounts is cleaning and preparing data. RapidMiner automates this process, offering tools to handle missing values, detect outliers, and manage data types. This automation not only saves time but also increases the reliability of the analysis. Use tools like “Remove Correlated” and “Remove Low Quality” (Fig 1), in addition to many other built-in tools, to drastically reduce the amount of time spent cleaning data.

Fig 1: Cleanse options in Altair RapidMiner Turbo Prep

2. Easy Integration with Multiple Data Sources:

With one of its biggest strengths, RapidMiner provides integration with a wide variety of data sources, including traditional databases (like SQL), big data platforms (such as Hadoop and Spark), cloud-based storage systems (like Amazon S3), and even data from spreadsheets or flat files. Also, with Altair Monarch now under the RapidMiner umbrella, it builds on its support for various data sources, including databases, Excel files, and cloud storage.

Users can also incorporate data from unstructured, or semi-structured sources like PDFs, Text files and websites.

Fig 2: Altair Monarch Data Source Options

3. Visual Workflow/Drag and Drop Interface:

The platform’s drag-and-drop interface allows users to visually represent their data processing workflows. This interface simplifies complex data processes, allowing users to visually build and edit their data workflows without the need for extensive coding experience (in most situations, no coding experience is needed), which makes it more accessible no matter what the skill level is.

The interface is divided into 3 main windows:

• Repository Window, where data, processes, and results are stored and managed.
• Operators Window, which houses a comprehensive library of data processing and analysis functions.
• Process Window, the main area where the workflow is created and edited.

Fig 3: RapidMiner Drag and Drop Interface

Enhancing Data Analysis

1. Wide Range of Analytical Techniques:

RapidMiner provides a comprehensive set of tools for statistical analysis, machine learning, deep learning, text mining, and more. Whether you are building a simple regression model or a complex neural network, RapidMiner has the necessary algorithms and validation methods.

In addition to the library of included tools, RapidMiner can integrate language models including Python, R, Java and many more. This integration makes it possible for the RapidMiner Marketplace to be available. The Marketplace is a group of extensions available to add functionality to RapidMiner for tools like web mining and deep learning.

Fig 4: RapidMiner Marketplace Extensions

2. Interactive Results Visualization:

RapidMiner's built-in visualization tools allow analysts to interactively explore data and model results. These visual tools help in uncovering patterns and insights that might be missed in traditional reports. Identifying those insights earlier in the data handling process, reduces a lot of additional rework in many cases.

Users can effortlessly generate a wide range of visualizations, including histograms, scatter plots, heat maps, and more, directly within the platform.

Because the entire Data Analytics and AI tool are rolled under the RapidMiner brand, users can also access Altair Panopticon, to create real-time dashboards and interactive content.

Fig 5: Visualization Options in RapidMiner

3. AI Hub:

The AI Hub is a centralized platform that serves as a shared workspace where data team members can collectively work on projects. Working in the collaborative environment gives users the ability to track project status, share data, processes and models, and schedule hardware-intensive processes on workstations or shared server resources.

It also supports a range of deployment options, from simple batch processing to real-time scoring, accommodating different business needs.

Fig 6: RapidMiner AI Hub Connections

4. Learning Curve:

The learning curve with many new applications tends to be one of the bigger obstacles companies need to get over before adopting a new software or solution. With RapidMiner, more users in a company can be trained, at no cost using RapidMiner Academy.

Users can be trained in basic Data Analytics concepts all the way up to advanced RapidMiner-based certifications.

Fig 7: RapidMiner Academy Landing Page

For many organizations that either have existing, manual workflows OR have loads of data that they don’t know how to organize, RapidMiner is worth checking out. From simplifying data preparation to enhancing model development and deployment, it offers a range of features designed to make its end users more efficient.