Location

Webinar: Online

Date

November 25, 2020

Duration

1 hour 

Time

9:30 a.m. - 10:30 a.m. 

Deadline for registration 

November 25, 2020

Prices:  

Webinar participation:

Members: Free
Non-members: Kr. 200,-

Unable to attend? 

If you for some reason are unable to attend the webinar, please let us know at meetinspire@dit.dk 

In many organizations, the frequent problem is that you have a lot of data, but what do you get out of it? Not utilizing data often occurs due to unawareness of its advantageous applications. Since all AI algorithms are built on a foundation of data, AI can leverage data as a resource, allowing the organization to utilize data to its full potential.

Several organizations have already figured out how to do this. But at this stage, AI has yet to generate real business value. The pressing issue at this stage is that the AI algorithms are not scalable and not taken into production and operation across the organization.

When it comes to successful AI projects, model deployment (also known as productionization) is key. At this stage, the developer facilitates autonomous operability of the AI algorithms, enabling it to run and scale without intervention and thus being utilized to its full potential. Algorithmic monitoring is furthermore also enforced during deployment to ensure sustainable operability of the AI algorithms.

Achieving a successful AI project entails a broad spectrum of efforts, ranging from from data collection, on which the AI algorithms are built, to productionization, where the value of AI is generated. One important aspect to keep in mind when embarking on an AI journey is that it encompasses multiple components. Specifically, the area of intellectual interest (the agenda set by academia) as well as the actual integration, production, and operations (the agenda set by the industry) are dictating the advancements in AI. It’s in the interplay between these two fields where the real value of AI is!

This webinar addresses applied AI with topics ranging from data ingestion, AI model development, deployment, as well as operation and illustrates how an AI platform lowers the barrier of entry by standardizing processes, and securing scalability of AI models across the organization. This seminar will provide insights into specific cases on how AI can improve revenue potential, increase productivity, and reduce risks within organizations.

Kristoffer Gordon Clausen, Data Scientist, 2021.AI 

As a Data Scientist at 2021.AI, Kristoffer has modeled multi-process manufacturing lines for pharmaceutical companies, predicted container dwell times for container port terminals, and deployed several models for telecom clients. Kristoffer is also a board member at Neural, a student-run society for AI in Denmark.