Where MLOps ends, deployment-to-production begins. Avassa allows you to deploy, monitor, observe, and secure containerized AI models and applications to your on-site edge. We empower you to develop, test, and release versions with unprecedented speed, no matter the number of locations. Avassa fills the missing gap of addressing the crucial step of deployment and operationalizing after your model development and build process. By utilizing standardized container technology, you benefit from the reuse of mainstream MLOps and container tooling and competence. By deploying your trained model as a container running at the edge, you also benefit from embedding all dependencies in one atom, making sure your trained model will behave as expected. Future-proof the benefits of your edge AI initiative with automated, remote deployment and operations at scale with an edge platform purpose-built for agile.
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Recent Content by Company
How Edge Computing In Retail Is Transforming the Shopping Experience
Forward-looking retailers are increasingly relying on an in-store combination of data collection through IoT devices with various types of sensors, AI for decisions and transactions on live data, and digital signage to communicate results and allow for interaction with customers and store associates. The applications built on this data- and AI-centric foundation range from more […]
Why Edge AI Struggles Towards Production: The Deployment Problem
There is no shortage of articles about how to develop and train Edge AI models. The community has also written extensively about why it makes sense to run those models at the edge: to reduce latency, preserve privacy, and lower data-transfer costs. On top of that, the MLOps ecosystem has matured quickly, providing the pipelines […]
“How to Right-size and Future-proof a Container-first Edge AI Infrastructure,” a Presentation from Avassa and OnLogic
Carl Moberg, CTO of Avassa, and Zoie Rittling, Business Development Manager at OnLogic, co-present the “How Right-size and Future-proof a Container-first Edge AI Infrastructure” tutorial at the May 2025 Embedded Vision Summit. In this presentation, Moberg and Rittling provide practical guidance on overcoming key challenges in deploying AI at the edge, including remotely managing containerized […]
