Our latest 9.6 release expands RapidMiner to full-time coders and BI users. Here we’ll show you the major enhancements made to our data science platform.
In supervised learning, model training uses data with known outcomes, while in unsupervised learning, the data doesn’t have a known outcome. So which is best for your use case? Read on to find out!
Resilience is the new accuracy in data science projects. Here’s why your “best” model might not be the best at all…
With Jupyter Notebooks baked into RapidMiner 9.6, coders have a powerful new tool to share projects with coworkers. Read on to find out all the details!
With our latest release, we’re letting anyone shape the future for the better, regardless of their background or skillset. Check out the highlights in this blog post.
Get a complimentary copy of the 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.
In this webinar, we cover the topic of process optimization through the lens of RapidMiner’s virtual optimizer—a real-time prescriptive dashboard that lets workers understand the current state of your operation, experiment with potential changes, and then implement the best solution.
Natural language processing is changing how companies understand their data. See what it can do for you.
Learn about RapidMiner Managed Server, our services offering to install, configure, and maintain a RapidMiner environment for you.
Read this 2018 Gartner Research Report detailing the challanges organizations face when deploying machine learning models into production.
In this webinar, Ingo dissects the issues that plague organizations striving to become ‘more AI-driven’ and prevent them from executing projects that have the potential to deliver incredible returns.
Looking to adopt AI in your manufacturing organization? Start with predictive maintenance – it rises above other use cases in terms of feasibility & impact.
We are proud to announce 5 new operators added across the Operator Toolbox and Smile extensions. Here’s an overview of these extensions and what’s new.
Machine learning and predictive analytics saved a petrochemical facility a million dollars in through optimizing parameters of a cracked gas compressor loop.
If you’ve spent a good bit of time replacing connections while moving a process to production, struggled with collaboration within your team, or have simply found the current feature set too rigid, we have good news for you.
Ingo discusses the need for anew approach to data science, machine learning, and artificial intelligence. Automated machine learning needs to guide analysts and not overrule their decisions.
This session presents a case study demonstrating a risk-based investment decision-making approach supported by machine learning for water distribution system assets.
This presentation shows how to leverage machine learning to detect and prevent fraud and make fraud fighters more efficient and effective.
Master Loyalty Group presents how they created a recommendation system within RapidMiner and the benefits they have seen from doing so.
This presentation covers typical data science roadblocks and how to overcome them, the optimal project structure and timeline for a data science project, and cross-industry examples and success stories of businesses at varying levels of data sophistication.
This presentation discusses Verizon’s Outlier detection system, which uncovers anomalies and then allows for a deep-dive into actionable insights.
This presentation outlines use cases from pure real time reporting to applying predictive analytics. For each use case we will show how they can be implemented using the Streaming Extension and the RapidMiner platform.
This presentation gives an overview of predictive analytics use cases at Lufthansa with some practical use cases from the airline industry like the prediction of arrival times.
The Pegasus Group Company discusses how they monitor and detect the presence of certain pathogens in the oceanic water, alerting the corresponding entities to take action and prevent the spread of these pathogens.
Learn how the Data Scientists teams and IT organization partnered at HPE, providing tools, technologies and processes to close the skill set gap between data science and business roles.
The hype around data science produces a dense fog that can easily restrict the broad scope of your vision, the rising slope of your applications, and the promised hope of new opportunities. This presentation busts those myths and shows you a better, simpler, and more rapid path to value and insights from your data.
Learn how to predict machine failure with RapidMiner and MapR.
Insights Driving Actions: The Role of the Business Translator in Choosing a Use Case with Clarkston Consulting
Learn about the role of the business translator and how they can help identify opportunities to use advanced analytics to solve problems.
Model validation is one of the most important aspects of the data science / machine learning process. In this video we will discuss two widely used visual approaches for comparing model qualities and will focus on how to connect the model with the business value it is supposed to create.
Read the Transcript 00:00 Good morning, I hope I can still say good morning. Good morning everybody, welcome to this presentation on the elimination of
Learn how Lufthansa increased the accuracy of their flight arrival time predictions using RapidMiner, saving significant costs associated with delays.
Learn how this manufacturer uses insights from RapidMiner to adjust its operations to reduce customer support costs and improve its customer experience.
Learn how TfL uses RapidMiner for the operation of the road network, managing the traffic signals and ensuring safe, high-quality roadworks across the city.
Learn how a US state auditor leveraged machine learning to detect and prevent the estimated one billion dollars wasted on fraud in healthcare per year.
Learn how the project partners identify huge potential for the application of machine learning to predict product defects early in the production line.
Learn how Daimler and Miele used RapidMiner to accelerate the product design and assembly planning phases in their factories to reduce time and cost.
Learn how LIAT uses RapidMiner to improve the time it takes to respond to customer issues and improve customer sentiment.
Learn how a LDC in the natural gas industry predicts which parts of its pipeline are at the greatest risk of failure with RapidMiner.
Learn how your organization can deliver data science and machine learning on Hadoop faster than ever before with RapidMiner and Microsoft Azure and HDInsights.
How Data Science Will Play a Pivotal Role in the Future of Equipment Maintenance with RapidMiner and MapR
According to Gartner 72% of manufacturing industry data goes unused due to the complexity of today’s systems and processes. Learn how to take advantage of IoT data in this webinar with MapR.
Getting actionable insights from unstructured content isn’t easy. Learn how RapidMiner and MonkeyLearn makes it easy to aggregate and analyze your all of your unstructured content.
Jeff Dwyer from ezCater demonstrates how they use Stitch and RapidMiner to make early predictions on LTV – customer lifetime value.
EY shares best practices on how organizations today are blending and drawing correlations from multiple data sources with data science to mitigate and overcome organizational risks.
Working with REST APIs can be cumbersome and challenging, in this webinar we demonstrate how to enrich and analyze chat conversations in RapidMiner Studio.
Learn how to help your marketing team turn customer data into predictions that will increase sales, optimize marketing spend, and make marketing overall more effective.
This webinar details how the partnership between RapidMiner and Talend is helping organizations operationalize predictive models in for use cases such as real-time customer experience, predictive maintenance, and fraud detection.
RapidMiner Data Scientist Dr. Fabian Temme holds a demo on a time series data set where he teaches users how to optimize their forcasting abilities
RapidMiner Founder Dr. Ingo Mierswa outlines how RapidMiner incorporates a novel approach for automatic feature engineering with RapidMiner Auto Model.
In this eBook, RapidMiner Founder and President, Dr. Ingo Mierswa covers: Multi-objective optimization: the secret to great modeling, methods for applying it in machine learning and feature engineering, and how to apply these methods in RapidMiner.
The goal of this book is to introduce you to data science by covering the fundamental concepts plus step-by-step guidance on practical implementations.
Machine learning is constantly making every stage of manufacturing more efficient and lucrative. Learn how to harness its power for your business.
Learn how to connect RapidMiner Auto Model with other applications through Zapier, which has connectors to nearly every application that exists.
Data Science: Concepts and Practice (Second Edition) by Vijau Kotu and Bala Deshpande is now available. Order your copy today.
Learn how predictive marketing analytics can help engage your audience in all the different stages of a customer journey and maximize lead conversion.
Learn how artificial intelligence (AI), machine learning (ML), and big data are changing the renewable energy sector by taking advantage of collected data.
Learn how to structure and analyze customer reviews by sentiment and topic with machine learning and natural language processing.
RapidMiner regularly releases new versions of RapidMiner Studio, Server and Radoop. Read the top 10 reasons to upgrade to RapidMiner 9.
Read through a demonstration of Turbo Prep and Auto Model by Ingo Mierswa to see how RapidMiner makes data prep and machine learning fun, fast, and simple.
One of the most frequent questions I get asked is: “Ingo, I am from Industry X and my data looks like Y and my colleague recommended to use model Z – what is your opinion on what model to use?” In this blog post, I explain a well-proven framework for model selection.
I’m thrilled to announce that RapidMiner is nominated for the Company of the Year in AI, Machine Learning and Blockchain Technology at the NEVY Awards.
We are excited to announce our partnership with MapR. This opens up data science possibilities for those who rely on MapR for managing their big data.
In Part 4 of this series we discuss multi-objective feature selection, which can be used for unsupervised learning & to identify best spaces for clusters.
RapidMiner Founder and President, Dr. Ingo Mierswa discusses multi-objective optimization in machine learning, as well as, methods for applying it with RapidMiner.
Multi-objective optimization is great for feature selection because we can find all potentially good solutions without defining a trade-off factor.
Evolutionary algorithm is a generic optimization technique mimicking the ideas of natural evolution with the concepts of crossover, mutation, and selection.
Feature selection can greatly improve your machine learning models. Learn about it’s importance in part 1 of this blog series.
Remove obstacles to developing useful machine learning outputs and how to gain insights with the integration of RapidMiner and Tableau.
RapidMiner 8.0 will bring new features that offer improved reliability and horizontal scalability, to which we will expand the platform and revamp Server.
Learn how RapidMiner and Tableau provide a complete solution for analytics teams. See how these two platforms can be applied in manufacturing.
We look at three use cases to demonstrate how RapidMiner Studio and RapidMiner Server can compliment each other and advance your call center operations.
Building models to predict customer actions is just the first step in implementing data mining and predictive analytics solutions. The integration of the models with
This webinar discusses the trend of the democratization of data science and how that further increases the risk for applying models in a wrong way.
RapidMiner was again voted as the most popular general data science platform and this is all thanks to our community of users!
This webinar shows you real world use cases for machine learning on Hadoop using RapidMiner’s new SparkRM capabilities.
What exactly are we doing with AI? Learn about what artificial intelligence and machine learning can do – and what it can’t do.
Delano Lima from Brazil won first place for his political tweet analysis project using the Rosette API and Rapidminer Studio in a Data Scientist Challenge.
k-Nearest Neighbors is one of the simplest machine learning algorithms. As for many others, human reasoning was the inspiration for this one as well.
Learn how modern predictive analytics helps manage and accelerate all phases of the predictive analytic process lifecycle, from source to result.
There is hardly a day where there is no news on artificial intelligence in the media, and people know shockingly little about it. Read to learn more.
Learn how to build a Sales Pipeline Analysis solution using RapidMiner, removing the bias that plagues traditional forecasting processes.
RapidMiner is a leader in both the Gartner’s Magic Quadrant for Data Science and the Forrester Wave: Predictive Analytics and Machine Learning Solutions.
Calculating model accuracy is a critical part of any machine learning project, yet many data science tools make it difficult or impossible to assess the true accuracy of a model. Often tools only validate the model selection itself, not what happens around the selection.
Customer service centers are dominated by voice interactions between customers and service center agents, who are the face of the company.
Watch this webinar to learn the strategies and techniques for integrating Data Science into your Business Intelligence platform.
Learn how k-fold cross-validation is the go-to method whenever you want to validate the future accuracy of a predictive model.
Learn how k-fold cross-validation is the go-to method whenever you want to validate the future accuracy of a predictive model.
We’ve put together a list of the top ten most useful tips and tricks from RapidMiner’s data science team, community and super-users. Check it out.
Learn how to prevent mistakes in model validation and the necessary components of a correct validation in regards to the training and test error.
A wrong validation leads to over-optimistic expectations for the model’s performance. Learn how to validate models correctly with our new blog series.
Using data visualization can tell a thousand words about your models to stakeholders. Discover how RapidMiner integrates with tools, like Qlik and Tableau.
Feature Generation and Selection is the next step on transforming your data and we have some handy operators to help you make this process fast and easy.
As Data Scientists, Engineers and Analysts, you have to routinely transform data from one type to another. RapidMiner makes converting data types easy.
The first companies to implement predictive maintenance and convert their vast data into actionable insights will gain a huge competitive advantage.
RapidMiner offers the option to export processes as scalable images in the Scalable Vector Graphics (svg) or Portable Document Format (pdf) file formats.
You must spend time on data exploration; you must think about the problem you’re trying to solve, bring the right data together and then inspect it.
Learn how to connect with a Remote Desktop and finish the installation of RapidMiner Server on AWS and start running your processes in the cloud.
Learn how to take advantage of the AWS cloud infrastructure environment to put RapidMiner Server in a place where it can run 24/7.
Easily access Federal Reserve Economic Data or FRED API data in RapidMiner Studio using only two operators: Open File and Read XML.
Learn how to configure Studio settings and add proper path properties to execute R and Python scripts in RapidMiner Server.
Exploring a new discipline is always a difficult task – that’s why we are proud to provide you with a Data Science Map to help you with this journey.
If you’re familiar with the Groovy script language, then the Execute Script Operator will quickly become a favorite. Learn more here.
Many data scientists and analysts don’t have a deep understanding of Hadoop, so they struggle with solving analytics problems in a distributed environment. Watch this webinar to learn best practices for extracting value from Hadoop.
Learn how Mobilkom uses RapidMiner to analyze the textual content of incoming customer requests and automatically determine the topic of each request.
The question isn’t RapidMiner vs R, it’s how to use them together. Learn tips and tricks for using RapidMiner with Python and R.
Learn how Hadoop big data in-Hadoop & in-memory approaches have positives factors when doing data science.
How do we detect if there’s a problem in our infrastructure? RapidMiner explores the use of customer feedback to predict and reduce IT disruption.
Use data science to predict qualified leads. Use our simple drag and drop data science platform to revolutionize sales and marketing processes.
Focus on predictive analytics to create more effective marketing. Here are just 5 ways you can use predictive analytics marketing.
See why organizations are investing in Qlik machine learning to be able to easily implement predictive analytics models into their business.
Data scientist Martin Schmitz talks about using RapidMiner Studio to do your own cluster analysis of Hearthstone card decks.