Ingo Mierswa, Founder & President at RapidMiner, Inc – Interview Series

Ingo Mierswa is the Founder & President at RapidMiner, Inc. RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. More than 625,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.

What was your inspiration behind launching RapidMiner?

I had worked in the data science consultancy business for many years and I saw a need for a platform that was more intuitive and approachable for people without a formal education in data science. Many of the existing solutions at the time relied on coding and scripting and they simply were not user-friendly. Furthermore, it made data difficult to manage and maintain the solutions that were developed within those platforms. Basically, I realized that these projects didn’t need to be so difficult so, we started to create the RapidMiner platform to allow anyone to be a great data scientist.

Can you discuss the full transparency governance that is currently being utilized by RapidMiner?

When you can’t explain a model, it’s quite hard to tune, trust and translate. A lot of data science work is the communication of the results to others so that stakeholders can understand how to improve processes. This requires trust and deep understanding. Also, issues with trust and translation can make it very hard to overcome the corporate requirements to get a model into production. We are fighting this battle in a few different ways:

As a visual data science platform, RapidMiner inherently maps out an explanation for all data pipelines and models in a highly consumable format that can be understood by data scientists or non-data scientists. It makes models transparent and helps users in understanding model behavior and evaluating its strengths and weaknesses and detecting potential biases.

In addition, all models created in the platform come with extensive visualizations for the user – typically the user creating the model – to gain model insights, understand model behavior and evaluate model biases.

RapidMiner also provides model explanations – even when in production: For each prediction created by a model, RapidMiner generates and adds the influence factors that have led to or influenced the decisions made by that model in production.

Finally – and this is very important to me personally as I was driving this with our engineering teams a couple of years ago – RapidMiner also provides an extremely powerful model simulator capability, which allows users to simulate and observe the model behavior based on input data provided by the user. Input data can be set and changed very easily, allowing the user to understand the predictive behavior of the models on various hypothetical or real-world cases. The simulator also displays factors that influence the model’s decision. The user – in this case even a business user or domain expert – can understand model behavior, validate the model’s decision against real outcomes or domain knowledge and identify issues. The simulator allows you to simulate the real world and have a look into the future – into your future, in fact.

How does RapidMiner use deep learning?

RapidMiner’s use of deep learning somethings we are very proud of. Deep learning can be very difficult to apply and non-data-scientists often struggle with setting up those networks without expert support. RapidMiner makes this process as simple as possible for users of all types. Deep learning is, for example, part of our Auto machine learning (ML) product called RapidMiner Go. Here the user does not need to know anything about deep learning to make use of those types of sophisticated models. In addition, power users can go deeper and use popular deep learning libraries like Tensorflow, Keras, or DeepLearning4J right from the visual workflows they are building with RapidMiner. This is like playing with building blocks and simplifies the experience for users with fewer data science skills. Through this approach our users can build flexible network architectures with different activation functions and user-defined number of layers and nodes, multiple layers with different numbers of nodes, and choose from different training techniques.

What other type of machine learning is used?

All of them! We offer hundreds of different learning algorithms as part of the RapidMiner platform – everything you can apply in the widely-used data science programming languages Python and R. Among others, RapidMiner offers methods for Naive Bayes, regression such as Generalized Linear Models, clustering such as k-Means, FP-Growth, Decision Trees, Random Forests, Parallelized Deep Learning, and Gradient Boosted Trees. These and many more are all a part of the modeling library of RapidMiner and can be used with a single click.

Can you discuss how the Auto Model knows the optimal values to be used?

RapidMiner AutoModel uses intelligent automation to accelerate everything users do and ensure accurate, sound models are built. This includes instance selection and automatic outlier removal, feature engineering for complex data types such as dates or texts, and full multi-objective automated feature engineering to select the optimal features and construct new ones.  Auto Model also includes other data cleaning methods to fix common issues in data such as missing values, data profiling by assessing the quality and value of data columns, data normalization and various other transformations.

Auto Model also extracts data quality meta data – for example, how much a column behaves like an ID or whether there are lots of missing values. This meta data is used in addition to the basic meta data in automating and assisting users in ‘using the optimal values’ and dealing with data quality issues.

For more detail, we’ve mapped it all out in our Auto Model Blueprint. (Image below for extra context)

There are four basic phases where the automation is applied:

– Data prep: Automatic analysis of data to identify common quality problems like correlations, missing values, and stability.
– Automated model selection and optimization, including full validation and performance comparison, that suggests the best machine learning techniques for given data and determines the optimal parameters.
– Model simulation to help determine the specific (prescriptive) actions to take in order to achieve the desired outcome predicted by the model.
– In the model deployment and operations phase, users are shown factors like drift, bias and business impact, automatically with no extra work required.

Computer bias is an issue with any type of AI, are there any controls in place to prevent bias from creeping up in results?

Yes, this is indeed extremely important for ethical data science. The governance features mentioned before ensure that users can always see exactly what data has been used for model building, how it was transformed, and whether there is bias in the data selection. In addition, our features for drift detection are another powerful tool to detect bias. If a model in production demonstrates a lot of drift in the input data, this can be a sign that the world has changed dramatically. However, it can also be an indicator that there was severe bias in the training data. In the future, we are considering to going even one step further and building machine learning models which can be used to detect bias in other models.

Can you discuss the RapidMiner AI Cloud and how it differentiates itself from competing products?

The requirements for a data science project can be large, complex and compute intensive, which is what has made the use of cloud technology such an attractive strategy for data scientists. Unfortunately, the various native cloud-based data science platforms tie you to cloud services and data storage offerings of that particular cloud vendor.

The RapidMiner AI Cloud is simply our cloud service delivery of the RapidMiner platform. The offering can be tailored to any customer’s environment, regardless of their cloud strategy. This is important these days as most businesses’ approach to cloud data management is evolving very quickly in the current climate. Flexibility is really what sets RapidMiner AI Cloud apart. It can run in any cloud service, private cloud stack or in a hybrid setup. We are cloud portable, cloud agnostic, multi-cloud – whatever you prefer to call it.

RapidMiner AI Cloud is also very low hassle, as of course, we offer the ability manage all or part of the deployment for clients so they can focus on running their business with AI, not the other way around. There’s even an on-demand option, which allows you spin up an environment as needed for short projects.

RapidMiner Radoop eliminates some of the complexity behind data science, can you tell us how Radoop benefits developers?  

Radoop is mainly for non-developers who want to harness the potential of big data. RapidMiner Radoop executes RapidMiner workflows directly inside Hadoop in a code-free manner. We can also embed the RapidMiner execution engine in Spark so it’s easy to push complete workflows into Spark without the complexity that comes from code-centric approaches.

Would a government entity be able to use RapidMiner to analyze data to predict potential pandemics, similar to how BlueDot operates?

As a general data science and machine learning platform, RapidMiner is meant to streamline and enhance the model creation and management process, no matter what subject matter or domain is at the center of the data science/machine learning problem. While our focus is not on predicting pandemics, with the right data a subject matter expert (like a virologist or epidemiologist, in this case) could use the platform to create a model that could accurately predict pandemics. In fact, many researchers do use RapidMiner – and our platform is free for academic purposes.

Is there anything else that you would like to share about RapidMiner?

Give it a try!  You may be surprised how easy data science can be and how much a good platform can improve you and your team’s productivity.

Thank you for this great interviewer, readers who wish to learn more should visit RapidMiner.