It’s a big time for us at RapidMiner—our new next gen, cloud-based platform that we’ve been working on for years is finally available to the public. We designed the new RapidMiner to help enterprises overcome the challenges that get in the way of data science success and support diverse AI teams composed of data scientists, business experts, and enterprise leaders alike.
We’ve noticed a lot of enterprises are under the misconception that data science is just for data scientists—we’re here to tell you that it’s not.
In this blog series, we’ll break down how the new RapidMiner (and AI in general) can be used to solve the most common challenges each role in the organization faces, highlighting the product features that most contribute to those solutions.
First up: let’s take a look at how our new platform supports enterprise leaders.
3 Roadblocks the New RapidMiner Helps Enterprise Leaders Overcome
Maybe you’ve faced challenges when trying to implement an AI initiative in the past, or maybe you’re trying to proactively identify what could go wrong. Either way, RapidMiner has you covered.
1. Fix the Data Skills Gap
Many companies who’ve been underwhelmed by the results of their AI initiatives are making a common (but understandable) mistake—they’re leaving data science exclusively to data science experts. This approach has a few key shortcomings but essentially, it boils down to two things:
- Hiring enough data scientists to support an enterprise is next to impossible.
- Without the right business context, even the best data scientists will have a hard time getting models into production.
For most organizations, data science works best as a team sport. To embed models throughout your business, you need to get data and business experts working toward a shared goal—which requires upskilling workers across your organization.
While data scientists understand how to explore and prep data, select the right features, and thoroughly validate models, it’s equally important for them to learn the context behind the data, what business problem they’re trying to solve, and what a successful outcome looks like for the enterprise.
Making data science accessible to business experts is equally important, too, as they can contribute their rich business understanding, enhance their data skills, and build more trust in AI solutions (more on this later).
How We Do It
The new RapidMiner provides tailored user experiences for everyone, whether they’re new to data science or a PhD.
No one needs unnecessary complexity—and our automated data science capabilities make data prep, model creation, and solution operationalization fast and intuitive for everyone, from novices to experts. Unlike most AutoML solutions, RapidMiner goes beyond high-level functions to ensure that the nuances of the data science lifecycle are automated, too.
Not only can non-coders benefit from the ability to create predictive models, but seasoned data scientists can rely on the automated experience to speed up the most tedious parts of the process while trusting that they’re not cutting corners.
For data science experts who prefer to code, RapidMiner offers a fully integrated notebook environment that leverages the power of Python and its accompanying open-source libraries.
The platform also simplifies governance by standardizing production into a central place. Team members and admins can easily unpack and audit projects by getting a complete view of lineage, full change history, and automatically-generated model explanations.
To bridge the gap between the automated and code-based experiences, RapidMiner has a visual workflow designer that enables users to create drag-and-drop models. Complete with over 1500 operators, the workflow designer goes beyond algorithm selection to replicate almost anything that can be done with code.
This means that, regardless of skill level, your teams can always create the strongest model for their use case. And, because each operator can be unpacked, inspected, and tuned, your team will never have to compromise on flexibility or transparency.
2. Build Trust in Models and Predictions
Despite all the hype around enterprise AI, many non-data scientists are still wary of their daily roles changing too much with AI or robots taking over their jobs. This lack of trust in data science is one of the most common reasons that promising models don’t get deployed.
While the skills gap is partly to blame, silos within the organization have been a sticking point long before data science was a topic of boardroom discussion. Without full transparency into what other departments are working on, how can you expect employees not to be skeptical of new technology invading their work?
As enterprises shape their data science programs, they’re having a hard time selling their initiatives to decision-makers because they don’t fully understand what processes are being affected and how they’re likely to change.
In theory, data science platforms can help to address this—many tools on the market are designed to “democratize” access to AI. However, so many of these tools are black boxes that provide little-to-no insight into how AI works, which usually creates more confusion than clarity.
The right tool should both prevent the erosion of trust and help organizations overcome the silos that get in the way of major project wins.
How We Do It
The new RapidMiner is designed to help enterprises build trust and understanding in data science work in several ways.
First, the platform acts as a trustworthy hub for all things data science. All data assets are centrally managed, which means that any authorized project participant or decision maker can access, analyze, and understand the data that’s being used to build solutions.
Work is also organized by use case, which not only helps new project participants get up to speed quickly, but also enables employees who take on similar projects in the future to learn from past successes.
In addition, RapidMiner creates a transparent environment around any processes that your team builds. Regardless of which interface a project participant uses to contribute (automated, visual, or code), all work is automatically logged back to visual workflows, complete with plain-English explanations for each process step.
The platform also supports proven explainability techniques that help to interpret exactly how given models interact with features and make predictions—both at the global level (how a model is making predictions as a whole) and local level (how a model arrived at a single prediction).
Powerful AI apps help build trust by allowing decision-makers to understand model results and performance in a hands-on way. You can use apps to see how models behave in certain scenarios, test model results against expectations, and run “what if” scenarios to explore every possibility.
Lastly, the new RapidMiner makes it easy for teammates to share, access, and comment on each other’s work, which breaks down cross-team silos, streamlines data science project management, and makes solutions easier to understand and reuse down the road.
3. Scale With Ease
As organizations get further along with their AI initiatives, their priorities will inevitably shift. Today, the desire for ease of use and integration with legacy systems is outweighed by the need for flexibility and alignment with a cloud-first strategy.
When working with enterprise customers, one of the most common concerns we hear is the ability to scale without introducing unnecessary (and in many cases, cost prohibitive) hurdles.
While data science is a major initiative, it’s far from the only thing in-house IT resources are asked to support. Organizations need a way to build flexibility and scale into their plans without overburdening IT departments, regardless of how mature they are.
The right data science platform should integrate with the systems organizations already have in place, while having the flexibility to adapt as those systems and processes change over time.
How We Do It
The new RapidMiner has been completely re-architected from the ground up as a cloud-first platform, with flexibility and adaptability top of mind.
Organizations can easily add users and adapt to changing demands with minimal friction—the fact that there’s no hardware to manage also makes implementations much simpler and enables distributed teams to access their work from anywhere using the device of their choice.
One of the biggest benefits is the immediate and pain-free access to product upgrades, which can be a time-consuming and headache-inducing process with on-premises software.
Companies will also see meaningful cost-savings in both the short and long-term under this model. As with any cloud product, upfront costs are minimal, and there are no ongoing hardware expenses to account for and manage—just the licensing cost. RapidMiner is also committed to providing near-zero downtime, and the redundancy of the cloud helps avoid costly outages and process disruptions.
Finally, the cloud platform’s enhanced ability to seamlessly integrate with common enterprise data sources, BI tools, and other systems simplifies the process of accessing relevant data and publishing results where they’re most useful—ensuring that your teams can get data science projects off the ground quickly and deliver powerful insights intuitively.
To Wrap Up
When enterprise leaders commit to an AI transformation, there’s a lot to juggle. Between employees resistant to change, silos throughout the organization, new software to learn, and technology integration concerns, it’s not always smooth sailing.
We built the new RapidMiner not to eliminate those challenges, but to give leadership the tools they need to face them head-on—to get employees more invested in data science, to enable cross-team collaboration, and to provide an intuitive, scalable platform that supports your enterprise as you grow for years to come.
If you’re looking for more info on how RapidMiner supports leadership teams or want to see the platform in action, check out our on-demand webinar, How the New RapidMiner Helps Enterprise Leaders Deliver the Promise of AI.
