Packaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect, Founder at Haaga-Helia UAS.
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// Abstract
AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations.
While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.
In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.
// Bio
Founder, AI Enablement | Senior Lecturer, Haaga-Helia University of Applied Sciences
Jukka Remes has 20+ years of experience in software, machine learning, and infrastructure. Starting with fMRI research pipelines in the late 1990s, he’s worked across deep learning, GPU infrastructure (IBM), and AI consulting (Silo AI), where he led MLOps platform development.
Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of a company focused on next-gen AI enablement through MLOps.
// Related Links
Open source -based MLOps k8s platform setup originally developed by my team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform
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Timestamps:
[00:00] Jukka's preferred coffee
[00:39] Open-Source Platform Benefits
[01:56] Silo MLOps Platform Explanation
[05:18] AI Model Production Processes
[10:42] AI Platform Use Cases
[16:54] Reproducibility in Research Models
[26:51] Pipeline setup automation
[33:26] MLOps Adoption Journey
[38:31] EU AI Act and Open Source
[41:38] MLOps and 8wave AI
[45:46] Optimizing Cross-Stakeholder Collaboration
[52:15] Open Source ML Platform
[55:06] Wrap up
