Make AI Collaborative With Dataiku

Dataiku Product Lynn Heidmann

When you think about collaborative AI, you might imagine humans and machines working together: Seamless interplay between humans and AI. The artificial intelligence does what it does best. That is, analyzing mass volumes of data and making that information accessible to the human (maybe through a prediction or recommendation).

On the other hand, the human is doing what the machine cannot. This might be natural language tasks like speaking to a customer. Or real-time, complex decision making, such as evaluating conditions to make an important decision.

This is certainly an admirable end goal, but it’s just part of the picture. When it comes to AI, collaboration is so much more than just augmented intelligence.

Making AI collaborative goes all the way back to the creation of that AI system:

  • How did it get built?
  • Who decided what data to use for the machine learning model?
  • Who is maintaining that system?
  • How was the system, or how will it be, tested?

Being successful with AI in the enterprise means extending the capacity to work with data far beyond data scientists or even analysts. It’s becoming increasingly clear that AI is a team sport. The more people involved not just in the use of AI, but in its creation, the more successful it will be. 

But building collaboration around data and AI into the framework of organizations isn't easy. It means educating people around AI capabilities plus providing the right tools to build, deploy, and monitor the AI technology itself. Enter: Dataiku, the platform for Everyday AI.

Dataiku has collaboration at its core, enabling data experts and domain experts to work together, building AI into their daily operations. Here are the top three ways Dataiku makes collaboration around data and AI a reality:

See collaboration in action in Dataiku in the video above.

Visual Data Pipelines & a Central Environment

Every data analytics or AI project is a series of steps. Essentially there's data, data gets transformed, maybe used for machine learning, and there is an output. The output might be anything from a simple visualization to a business application. It can be hard to get everyone to trust the output of AI without everyone understanding this underlying pipeline.

The Dataiku Flow is the visual representation of a pipeline and the canvas where teams collaborate on data and AI projects. Consistent visual language, common objects, context tags, annotations, and more all ensure a clear understanding of each and every project. 

Bonus: Having all project knowledge centralized in a single place is also helpful in the context of responsible and explainable AI. Dataiku can automatically generate comprehensive documentation for both models and the project flow on a one-off or scheduled basis. Customizable templates include all the metadata and visualizations needed to snapshot the project state or model design and results.

Access for All

Successful human-AI collaboration means giving more people a seat at the table from the very beginning of the data pipeline process. It is not enough to enable individuals with similar backgrounds to work together. Organizations must bring all of the different people together who have relevant input.

This includes, of course, the data science and machine learning experts. But also must include the individuals who are experts in their fields, be they actuaries, marketing analysts, or shop-floor technicians.

Dataiku provides a unique environment where both coders and non-coders can contribute. Think about a data scientist and, for example, an expert in building jet engines, working with data on the same platform. At GE Aviation, this is exactly what is happening. Their self-service system is leveraged by engineers who use data from the self-service system to redesign parts and build jet engines more efficiently. The commercial group (by leveraging data scientists) uses the tools to transform engine sensor data from customers and build analytics services for them. 

With features like built-in project wikis, teams document their motivations, methods, and decisions to preserve critical context and provide continuity. Robust knowledge management is just one of the ways Dataiku democratizes AI. A timeline of recent actions, to-do lists, and built-in chat functions also keep contributions around AI projects centralized.

Packaged Assets

It's not enough to just provide access to data and AI projects for all. Collaborative AI also requires providing the right tools and outputs to make data and AI meaningful for everyone. For example, Dataiku facilitates interactive data exploration, dashboards, and custom applications.

More specifically, coders and other power users can empower cohorts of analysts or subject matter experts. By packaging up subflows or Python code as custom recipes, less technical users can perform tasks (whether simple or advanced) in an easy-to-use visual interface. Further, it helps with inter-team collaboration for data scientists, ensuring reusability across projects. 

With Dataiku, the key is providing something for everyone. Coders appreciate the shared bank of useful code snippets and libraries. With Dataiku, they can expedite scripting tasks and ensure everyone is applying consistent methodology for data manipulation.

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