Discuss Linux Foundation CNPA Exam Topic 5 Question 19

How can an internal platform team effectively support data scientists in leveraging complex AI/ML tools and infrastructure?

AIntegrate AI/ML steps into standard developer CI/CD systems for maximum reuse

BImplement strict resource quotas and isolation for AI/ML workloads for stability.

COffer workflows and easy access to specialized AI/ML tools, data, and compute.

DFocus the portal on UI-driven execution of predefined AI/ML jobs via abstraction.

Suggested Answer: C

The best way for platform teams to support data scientists is by enabling easy access to specialized AI/ML workflows, tools, and compute resources. Option C is correct because it empowers data scientists to experiment, train, and deploy models without worrying about the complexities of infrastructure setup. This aligns with platform engineering's principle of self-service with guardrails.

Option A (integrating into standard CI/CD) may help, but AI/ML workflows often require specialized tools like MLflow, Kubeflow, or TensorFlow pipelines. Option B (strict quotas) ensures stability but does not improve usability or productivity. Option D (UI-driven execution only) restricts flexibility and reduces the ability of data scientists to adapt workflows to evolving needs.

By offering AI/ML-specific workflows as golden paths within an Internal Developer Platform (IDP), platform teams improve developer experience for data scientists, accelerate innovation, and ensure compliance and governance.

--- CNCF Platforms Whitepaper

--- CNCF Platform Engineering Maturity Model

--- Cloud Native Platform Engineering Study Guide

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12 comments · newest first

Willis 2 months ago

Option B seems too restrictive. We need flexibility for AI/ML workloads.

Albina 2 months ago

I agree, but A could really streamline the process. CI/CD integration is key.

Whitley 2 months ago

I think option C is the best. Easy access is crucial for data scientists.

Amie 2 months ago

Totally agree with A), reuse is key in development!

Barbra 3 months ago

Wait, D) sounds like it could oversimplify complex tasks?

Adela 3 months ago

B) seems too restrictive, might hinder innovation.

Corazon 3 months ago

C) is definitely the way to go for accessibility.

Adelaide 3 months ago

A) is a game changer for efficiency!

Antione 3 months ago

Focusing on UI-driven execution sounds like a good way to simplify things, but I feel like it might not cover all the complexities of AI/ML workflows.

Ryan 3 months ago

Implementing strict resource quotas seems like it could help with stability, but I wonder if it might limit flexibility for data scientists.

Tennie 4 months ago

I think offering workflows and easy access to tools sounds familiar from our practice questions, but I can't recall if it was the most emphasized point.

Therese 4 months ago

I remember discussing how integrating AI/ML steps into CI/CD could really streamline the process, but I'm not sure if that's the best option here.

Linux Foundation CNPA Exam - Topic 5 Question 19 Discussion