Akkio was created by executives from Sonos, a wireless home audio company, and Markforged, a smart additive manufacturing company, out of a common frustration: getting vast quantities of data but slow ways to use it to drive the business.
FREMONT, CA: Akkio, a no-code machine learning (ML) platform for modern sales, marketing, and finance teams, has declared that it has introduced a new feature called Data Stories. Data Stories allow Akkio users to understand the inputs that are most predictive in driving the AI models they create. Data Stories assist users in overcoming the "black box feeling," or a lack of faith and trust that many non-data scientists have while developing predictive models.
Using Akkio's no-code ML architecture, any technically savvy user – not just data scientists and software engineers – can create and deploy AI predictive models. By making AI easy to use, Akkio opens up the power of AI to thousands of new applications where AI might be useful right now. These new users, on the other hand, need different methods than data scientists for comprehending and trusting model performance. This is where Data Stories can be of assistance. Thanks to Data Tales, customers can now see, understand, and act on their business data like never before.
As per a PWC report titled, "AI predictions 2021," 52% of companies have accelerated their AI approach in the wake of the COVID-19 crisis and AI is paying off for them in concrete ways. They stated, "the future payoff is even greater and could give early adopters an edge that competitors may never be able to overtake." Companies do not have time to solely build AI projects from the ground-up. In order to keep up, they need to utilize platforms like Akkio that will get the results faster.
Akkio was created by executives from Sonos, a wireless home audio company, and Markforged, a smart additive manufacturing company, out of a common frustration: getting vast quantities of data but slow ways to use it to drive the business. It wasn't for lack of trying, either. Previously, Akkio's founders used custom solutions implemented by consultants – the kind that cost a lot of money and took a long time to complete – and employed hard-to-find and expensive-to-keep data scientists. However, the backlog of promising projects that were left unstaffed only increased. Finally, they needed a self-serve machine learning platform that any department with data could use to take care of the situation. They built it when they couldn't find it.