Step #4 roadmap
In the final step of our AI Roadmap, we look at whether the previous steps have finally led to the desired effect. Is the model doing what it was trained to do? Does it deliver in practice what it should? Did all the inputs finally lead to that output that you can move forward with?
The final step in our roadmap
In previous blogs, we have talked about perceiving, structuring and understanding as the first three of the total four steps that make up Datacadabra's AI roadmap. The final blog on our roadmap focuses on execution. In Step 4, we link automated actions to the conclusions from Step 3, through software integrations. In other words, what will be the output when a tested model is ready to be put into production? And how will we process, register and secure that output?
We do this through:
- Reports
- Dashboards
- API integrations
What is an API?
With the first two, you can no doubt imagine something. By API integration, we mean a process by which two or more applications that perform a common function exchange data with each other. In doing so, the idea is to facilitate interaction between data, devices and systems.
Output in GIS maps
For step 4, let's take as an example the MowHawk, the smart camera system we developed in co-creation with Wim van Breda on the arm of a mower. The output of the MowHawk is displayed in GIS (Geographic Information System) maps. Using GIS, it is possible to manage, analyze and share various data with others.
The output of what the cameras have recorded arrives in the form of GIS map layers directly to ecologists in the office. As a result, they no longer have to inspect the roadsides themselves, saving a considerable amount of time and money. And municipalities are better informed about future roadside management. Has a lot of litter been spotted somewhere? Then you can send people there to take action.
AI as a solution
In more and more places, at an increasing number of organizations and in more and more products, AI solutions are being successfully implemented to support humans in their daily work.
What challenges do these companies face? And what tasks can technology take over? On the one hand, for example, we see increasing demands for themes around sustainability, biodiversity and inclusiveness.
In the medical sector, we also expect an increasing demand for support using AI. On the other hand, there are increasing demands for accountability, insight and reporting. All these requirements create a lot of extra work that the existing workforce cannot handle and for which hiring capacity is difficult and expensive. AI can then provide a helping hand.
Are you also interested in the application of AI for your company or product? We would love to talk to you about the unlimited possibilities of AI. Contact us for a consultation or a customized workshop.
The white paper DIF in your mailbox?
Datacadabra has created a white paper in which we explain how the DIF works. Using an example, we take you step-by-step through the process of structuring and understanding data perception so that models can be trained on it.
Curious about the whitepaper? Fill in your details below and you will receive our white paper on the DIF in your email
