Packaging MLOps Tech Neatly for Engineers and Non-engineers

Explore how to make MLOps technology accessible to both technical and non-technical teams in this 56-minute podcast episode featuring Jukka Remes, Senior Lecturer and AI Architect at Haaga-Helia University of Applied Sciences. Discover the challenges of implementing MLOps in organizations with limited engineering resources, particularly SMEs and research teams, while understanding why robust MLOps practices remain essential for experiment management, GPU compute sharing, model tracking, and AI regulation compliance. Learn about an open-source MLOps platform originally developed at Silo AI that can be deployed across diverse environments from laptops to supercomputers, featuring Git-based workflows and CI/CD automation that allows users to focus on model development rather than infrastructure management. Gain insights into AI model production processes, platform use cases, reproducibility in research models, pipeline automation, and the MLOps adoption journey. Understand the implications of the EU AI Act for open-source projects and explore strategies for optimizing collaboration between technical and non-technical stakeholders in AI projects. Access practical knowledge about cross-platform MLOps deployment and discover how shared setups can enhance collaboration, productivity, and computational efficiency in AI development workflows.