Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // MLOps Podcast #322

MLOps.community · Beginner ·📐 ML Fundamentals ·11mo ago
Packaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect at Haaga-Helia UAS, Founder & CTO at 8wave AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // 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 & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied Sciences Jukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also 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 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI. // Related Links Open source -based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any u
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 12 Bare necessities for getting an ML model into production
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 13 MLOps and Monitoring
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 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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 27 Developing a Machine Learning Feature Store
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 29 3 key parts to Machine Learning monitoring
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 32 MLOps: Airflow Pros and Cons
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 33 Specific challenges in Machine Learning
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 34 Current State Of Machine Learning
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 36 Learning from real life Machine Learning failures
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 37 Survivorship Bias in machine learning tutorials
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 38 Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
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 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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 43 Speed, Trust, Evolution and Scale in MLOps
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 46 Deeper thinking from data scientists around platform blackholes
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 47 Checkpointing, metadata, and confidence in your data
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 48 Adjacent usecases and multistep feature engineering
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 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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 50 Reproducability flaws in end to end Machine Learning debugging
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 51 3rd wave of data scientists
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 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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