Data-driven organizations – Best practices for AI operationalization in Sweden

Fifteen of AI Sweden's partners from the private sector, public sector, and academia will address challenges related to large-scale deployment of AI solutions and the broad application of AI across organizations. 

Challenges

The technical challenges encompass changes in both data and models. Key questions include: 

• Will new data interact with a current model in the same way as existing data? 
• Will an updated model interpret data consistently with its predecessor? 
• How can models be effectively retrained with new data?

From an organizational perspective, key considerations include defining necessary roles and responsibilities, establishing processes to guide AI toward appropriate objectives, and determining prerequisites for new projects, data utilization, and technology adoption.

Objective & expected outcomes

The overarching goal is to understand how organizations should adapt as data and technology evolve, and to establish a framework for functional and scalable MLOps, facilitating AI implementation in Swedish organizations, encompassing necessary processes and organizational adaptations.

The project aims to develop tools and guidelines that help businesses implement suitable organizational structures and technologies to work data-driven beyond the prototype and testing stages. 

Supplier parties will contribute by establishing a sandbox for MLops to test different solutions. 
Other parties will contribute with use cases and best practices from their operations.

For more information, contact