Introducing Deepflow, the 1st low-code data science platform. No coding or IT required.

Working with data always feels like being in the trenches. Thankfully, today’s technological landscape bares almost no resemblance to what us, the data scientists of the stone age, had to work with. Looking back at my first machine learning (ML) application, there was as much coding as there was finger-crossing and self-inflicted pep talks. But 10 years after deploying my first ML algorithm in production I still feel like the learning curve for implementing common use cases is too steep for beginners.

Common AI strategy

During these years, I’ve had the opportunity to design and implement various machine learning applications ranging from pharmacology clinical trials to recommender systems in retail as well as fraud detection in telecom and finance.

After witnessing the good, the bad and the ugly of what AI has to offer, I realized that while developer tools have continuously improved, more accessible solutions have not been prioritized. And it is not without consequences.

Today, a widespread lack in accessible data science tools often leaves domain experts out of the loop, resulting in data scientists and engineers setting the pace for AI adoption in their organizations. And it is causing problems. Even with fancy PR doing a great job at praising the miracles of enterprise AI, reality remains far less glorious, 87% of AI projects never make it to production. It’s an open secret among data practitioners. Afraid to miss the AI train to the benefit of their competitors, too many companies keep throwing resources at the problem in hope for breakthroughs to emerge. Lack of business-AI alignment is poised to set failed AI strategies on repeat.

Aim for the moon. Fail. Repeat.

Enterprise AI is on a collision course with reality and it is only a matter of time before companies wake up to the fact that 80% of their AI projects resemble alchemy, run by wizards whose talents will not scale within their organization.

Tomorrow’s leading companies will be the ones able to put data science in the hands of ALL their employees. Empowering domain experts is the best strategy to make sure organizations steer away from what Cassie Kozyrkow, Chief Decision Scientist at Google, calls “Machine Learning: doing stupid things faster with more energy”.

That is why we built Deepflow, the first low-code platform to design and run data science workflows 100% visually. No coding or IT required. With our unique approach we strive to future proof companies by making sure AI capabilities are spread evenly between employees, no matter their technical skills. As the data science community grew, we replaced Java by Python and Weka by Tensorflow, now it is time for data science to embrace its “spreadsheet revolution” and truly become a commodity.


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