MLOps for DevOps People

DevOps and Docker Talk: Cloud Native Interviews and Tooling

Nội dung được cung cấp bởi Bret Fisher. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được Bret Fisher hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.

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Bret and Nirmal are joined by Maria Vechtomova, a MLOps Tech Lead and co-founder of Marvelous MLOps, to discuss the obvious and not-so obvious differences between a MLOps Engineer and traditional DevOps jobs.

🙌 My next course is coming soon! I've opened the waitlist for those wanting to go deep in GitHub Actions for DevOps and AI automation in 2025. I'm so thrilled to announce this course. The waitlist allows you to quickly sign up for some content updates, discounts, and more as I finish building the course. https://learn.bretfisher.com/waitlist🍾

Maria is here to discuss how DevOps engineers can adopt and operate machine learning workloads, also known as MLOps. With her expertise, we'll explore the challenges and best practices for implementing ML in a DevOps environment, including some hot takes on using Kubernetes.

There's also a video version to watch on YouTube.

★Topics★
Marvelous MLOps on LinkedIn
Marvelous MLOps Substack
Marvelous MLOps YouTube Channel

Creators & Guests

Cristi Cotovan - Editor
Beth Fisher - Producer
Bret Fisher - Host
Maria Vechtomova - Guest
Nirmal Mehta - Host

(00:00) - Intro
(02:04) - Maria's Content
(03:22) - Tools and Technologies in MLOps
(09:21) - DevOps vs MLOps: Key Differences
(19:22) - Transitioning from DevOps to MLOps
(22:52) - Model Accuracy vs Computational Efficiency
(24:46) - MLOps with Sensitive Data
(29:10) - MLOps Roadmap and Getting Started
(32:36) - Tools and Platforms for MLOps
(37:14) - Adapting MLOps Practices to Future Trends
(44:08) - Is Golang an Option for CI/CD Automation?

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