Recent recognition received from Gartner and RapidMiner’s end-users
If you follow RapidMiner’s blog, you know that we shy away from self-promotion. We’re much more interested in lending expertise that can help your data science efforts—model creation best practices, cutting-edge industry examples of AI/ML usage, and recommendations on how to overcome common business challenges that can prevent operationalization of machine learning.
However, we’ll make exceptions when our product team gets some much-deserved recognition. Their work empowers people to leverage machine learning more effectively, regardless of their role or technical skill-level. They make it easier to solve business problems and tackle new challenges through a more informed, analytical lens.
So, with that said…
RapidMiner Named a Visionary in this year’s Magic Quadrant for Data Science & Machine Learning Platforms
We’re excited to share that Gartner has named RapidMiner Visionary in this year’s Magic Quadrant for Data Science & Machine Learning Platforms! If you’re unfamiliar with Gartner Magic Quadrants, they offer an in-depth look at markets, the trends that define them, and key participants. Put simply, Magic Quadrant help companies get a sense for vendors’ strengths and weaknesses when they’re evaluating a purchase. By providing actionable insights and advice, they ensure that readers are making the most informed buying decisions they can.
We believe this year’s Magic Quadrant builds on the positive trends that we saw in 2020’s Gartner Peer Insights for Machine Learning and Data Science, which is the firm’s ranking of platforms in the market based on real end-user reviews. The feedback from RapidMiner users was so positive that we were awarded a Customers’ Choice Award, signifying that customers rated our platform strongly relative to competitors in the market. We’ve seen similar feedback from other reviewers on Forrester & G2 Crowd.
Over the past year, we’ve been focused on expanding our platform to meet the needs of larger enterprises. An examination of the challenges that these enterprises face when trying to use AI led us to invest in key areas like collaboration, governance, and explainable AI. The recent recognition we’ve received from both Gartner’s analysts & RapidMiner’s end-users validates our investment in those areas.
What Does it Mean to be Visionary?
The reason that we view this as such a key milestone isn’t just because we were recognized, but it’s because of the advancements that we’ve made in the realms of multi-persona collaboration, explainable AI, and model governance.
In terms of collaboration, it’s always been a goal of ours to allow data scientists and business experts to work together to solve business problems because, while data scientists bring a wealth of knowledge to the table when it comes to building models and extracting insights, they don’t have experience working in functional areas every day, so they may not have as much context about business problems. By contrast, a Head of Production is in tune with the fact that they need to lower product defects & improve yield; they just don’t have the coding background to build models than can give them insight on where to start.
By allowing these two groups to work together in a single platform, companies using RapidMiner can ensure that they’re addressing the right problems and building technically sound models that will have strong business impact.
In addition to getting teams to work better together, we’ve also been focused on ways to help you visualize and communicate the results of your data science work to others. We’re well aware of the fact that if can’t easily explain what your model is doing and how, it’s unlikely that you’re going to be able to get it implemented to have real business impact. And what better way to present model insights than with visualization?
By enabling users to visualize and explain the models that they’ve built, whether they’re in development or production, RapidMiner creates greater transparency, gives users full control over insights, and helps ensure that models can make it across the finish line and return dividends on the investment in machine learning.
Lastly, we’ve invested a lot into helping companies establish secure, governable data science practices. This is rooted in the belief that no enterprise AI initiative is worth investing in if you can’t guarantee that your data is safe and properly governed. That’s why we’ve implemented auditable project-tracking, Single Sign-On (SSO), and strong identity and access management (IAM) capabilities within our platform, allowing admins to secure their AI pipeline, all in one place.
In addition to the three main points above, the Magic Quadrant also notes RapidMiner’s clear vision for what features need to be implemented in the future, as well as our ability to get those features right. For example, in 2020, we notably added capabilities that “enable users to perform automated feature engineering, and share and store features across an organization, thus enhancing reusability and reproducibility.”
Again, while the posts on this blog aren’t typically about RapidMiner, we’re proud to be recognized for our commitment to reinvent enterprise AI so that anyone has the power to positively shape the future. Check out everything Gartner has to say by reading the full report.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner Magic Quadrant for Data Science and Machine Learning Platforms, Peter Krensky, Carlie Idoine, Erick Brethenoux, Pieter den Hamer, Farhan Choudhary, Afraz Jaffri, Shubhangi Vashisth,1st March 2021.
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