Data-driven organizations
Best practices for AI operationalization in Sweden
Over 20 months, leading Swedish organizations from industry, academia, and the public sector joined forces to figure out how to move AI from experimentation to real-world implementation. Together, they identified the organizational, technical, and governance models that enable responsible, scalable, and efficient AI operations.
Why? Thanks to AI, organizations now have a toolbox that lets them create solutions that were previously impossible. Over the last few years, this new solution space has been explored through proof-of-concepts and pilots at many of our partners. This phase has been needed to get an understanding of how AI can create value in various real world cases.
But once you have an idea on what you want to do, you face the question on how to continue development and put models into production in a structured and organised way on scale. Hence the project Datadriven Organizations: Best practices for operationalizing AI in Sweden (DDO) was formulated.
This project has been co-funded by participating partners and Vinnova. AI Sweden is in part financed by the European Regional Development Fund under the project "Increased national collaboration and accelerated use of AI in all industries".
Use case presentations
The majority of DDO’s efforts was directed towards three very concrete use cases that project partners representing needs owners in the project brought from their own organizations: How to use AI in a sustainable way, how to use shared infrastructure between different applications in a way that is regulatory compliant, and how to manage and administer a situation where you have thousand of models in production?
Case 1: Sustainable AI infrastructure lifecycle
How can we make AI adoption economically viable and sustainable across its entire lifecycle, even with limited resources? Through a collaboration between Region Halland and Aixia, concrete benchmarks were conducted for both text and image analysis, comparing energy efficiency etc for hardware, models, and frameworks.
Case 2: 1000 models in production
How do you scale ML operations from a few to hundreds or even thousands of models while maintaining efficiency and governance without scaling the needed personnel to support the operations? Led by Volvo Parts and experts from Hopsworks, Red Hat, and Linköping University, the use case uncovered practical strategies to manage the life-cycle of countless models without proportional human resource growth. Discover strategies for successful scalable ML operations in complex business environments.
Case 3: Centralized AI Infrastructure with Kubernetes: Secure and Compliant
Trafikverket needed to securely pool fragmented, specialized resources like GPUs while meeting a large array of legal requirements including MSB’s stringent segregation requirements. This use case developed a proposed robust architecture, validated through multiple proofs-of-concept utilizing Stormgrid, Red Hat and Proact technologies. Discover a secure method to share IT resources between development, test and production that enhances capacity and modernizes practices for AI-based products.
Whitepapers
Watch this space: Resources will be available by December 16
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Technical demos
Complementing our white papers, we present the project’s core findings in video format. In this section, you will find dedicated presentations of each use case, alongside in-depth technical deep dives and demos that illustrate how these frameworks are implemented in practice.
AI Operation Talks: Insights from the Frontlines of Deployment
While the project defines the frameworks for governance and structure, the AI Operation Talks series showcases the practical reality of building AI-driven organizations. Curated from the vibrant Swedish AI community, this collection features "TED-style" presentations from the experts, engineers, and entrepreneurs currently navigating the shift from experimentation to production.
AI Operations talk playlist: Watch all 21 recordings from this webinar series.
Want to deep dive even further?
Several of the project partners provide more information on their websites, explore below.
Blog post by Red Hat
The MLOps Challenge: Scaling from one model to thousands: What if managing models didn’t have to be chaotic?
Book on MLOps by Jim Dowling at Hopsworks
Building Machine Learning Systems Batch, Real-Time, and LLM Systems
An article written by Tiger et al during the project
Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects.
Project partners
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