Tension between Platform teams and Data scientists

MLOps.community · Intermediate ·📄 Research Papers Explained ·1y ago

Key Takeaways

The video discusses the tension between platform teams and data scientists, and how to bridge the gap between them using a platform as a service as a critical middleware, highlighting tools like Jupyter Notebook and Kubernetes.

Full Transcript

[Music] there's always this tension between uh folks like us and many cases it so how do we solve this so in summary these are the five core issues that organizations struggle with and users struggle with right everyone is struggling with it and there's got to be a solution to this problem so how do we Bridge these gaps right you the users and the AI infrastructure and this it in between who today is not able to provide that bridge they want to build that bridge desperately build that bridge so what we believe is needed is you need a platform as a service as a critical middleware that acts as that bridge between the users and it owned AI infrastructure the it owned AI infrastructure could be gpus could be VMS could be kubernetes clusters could be data because as users on the left I just want to fine tune I just want to build my model I want to use my jupyter notebook why do I have to go learn kubernetes so what do we do now just to kind of summarize right the the Gap here is you as users want to use one of these I think most of you here are probably extremely familiar with these tools on the top and some of you might be building your apps on top of this right not using these platforms but building your own apps and right at the bottom you have accelerated Computing infrastructure and theany company says well we got some gpus right maybe 50 gpus and uh what you need in the middle is this pass what does that pass do really right it will help with two problems orchestration and governance and consumption and monetization and you might be thinking what the hell does that mean really right what this really means is this right as a user you want to be able to like literally click a button and get the right kind of infrastructure instantly how is ITP going to manage all these things and think about those poor souls right when they're going to struggle right like managing all these various asks that everyone has and they have a limited pool of resources below so they need to make make sure that they operate this efficiently so what would that look like the answer for this is you got to centralize there's no choice right like and this is something that you know we kind of been working with a lot of lar Geno prices and this is the answer that they have settled on to

Original Description

How to bridge the gap between data scientists, ML researchers and ML platform teams. This talk was part of our AI in Production virtual conference. Huge shoutout to Rafay.com for sponsoring the event. Full talk here: https://home.mlops.community/home/videos/bridging-the-gap-between-model-development-and-ai-infrastructure-mohan-atreya-ai-in-production-2025
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The video discusses the tension between platform teams and data scientists, and how to bridge the gap between them using a platform as a service as a critical middleware. It highlights the importance of orchestration, governance, consumption, and monetization in MLOps. By implementing a platform as a service, data scientists can focus on model development without worrying about AI infrastructure management.

Key Takeaways
  1. Identify the gap between data scientists and platform teams
  2. Implement a platform as a service as a middleware
  3. Configure the middleware for orchestration and governance
  4. Use Jupyter Notebook for model development and fine-tuning
  5. Manage AI infrastructure resources using Kubernetes
💡 A platform as a service can act as a critical middleware to bridge the gap between data scientists and platform teams, enabling efficient model development and AI infrastructure management.

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