In a previous post we looked at why enterprise AI is broken and introduced Deepflow, our solution to put data science in the hands of non-developers.
Traditionally, there are two ways your organization can adopt AI at scale. You can go the Horizontal or the Vertical route. Horizontal AI tools are made to be flexible and use case agnostic. They are development platforms that give you full control over your data science workflows, from conception to deployment. On the other side you have Vertical AI tools. Made for specific use cases, these tools are designed to solve one problem and one problem only. They do it well and the learning curve is often little to non-existent.
Once an organization sees the value in using already existing AI tools instead of reinventing the wheel, the choice between adopting Horizontal or Vertical tools will fall on who owns AI initiatives internally. Data scientists and engineers are accustomed to having flexible tools with great levels of customization. If they choose on behalf of everybody else, they will naturally push for implementing Horizontal tools, de facto excluding anyone not on the technical side. Horizontal solutions, by wanting to be a one-stop-shop for everything AI, create a new set of problems. Customizing them quickly turns into maintaining expensive Frankenstein softwares and it makes it even harder for your team to move from research to production. So what about Vertical tools then? Well, their ease-of-use cannot exist without a lack of transparency. Most of them suffer from a black box approach, forcing you to trust the outputs blindly. They are often hard or impossible to customize and their pricing models makes their cost at scale increase exponentially.
It should be clear by now that the path to enterprise AI is a catch 22 for most companies. Favoring accessible solutions makes you lose freedom while flexible solutions lock non-technical employees out. The idea behind Deepflow stems from this conundrum. We decided to combine the level of customization offered by Horizontal solutions with the ease-of-use unique to Vertical tools. And to achieve this we had to rethink how people approach working with data. That is why Deepflow is built around the concept of Flows, a brand new way to experience data science by approaching every aspect of working with data through a drag and drop graphical interface.
Go with the Flow!
In Deepflow, a Flow is what we call an end-to-end data science workflow. But that is just scratching the surface. Flows are much more powerful than that. Flows are designed to be:
- Always production-ready
Flows are Visual
Work with data visually from start to finish. Flows are designed to get you from data to AI quickly and without a single line of code. Their visual nature is the antidote to AI’s black box problem. Flows are transparent and auditable by design. Spend time thinking about high level data problems, rather than mindlessly repeating busy work.
- Drag & drop interface made for humans
- Automate end-to-end data work visually
- Build trust in AI with transparent processes
Flows are collaborative
Contribute together as a team in real-time. Flows empower everyone in your team with a shared visual language. Domain experts now have the possibility to design and prototype in tandem without engineers in the loop. Bring your ideas to life faster and test concepts earlier and more often. Show the world, don't tell.
- Edit and comment alongside your co-workers in real-time
- Allow non-developers to create value with data
- Stay on track with auto-save and version history
Flows are always production-ready
Deploy in 1 click and iterate faster. Flows are designed to bring your projects from concept to production even faster. They are always 1 click away from being deployed and they scale automatically. Everyone in your team can easily reuse components and iterate from previous projects. Do not reinvent the wheel!
- All-in-one DataOps
- Manage team members with user permissions
- Endless possibilities with Plugins & API