Episode Details

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 new GitHub Security workshop has launched! A free 2-hour workshop with hands-on labs to harden your repos and your workflows from common supply chain attacks. I'll cover how attackers are getting in, and then we'll lock down a sample repo so you know what needs to be done to protect your code. You'll leave with a deep understanding of risks and mitigations as well as a list of helpful tools to keep your repos safe, including my new "gasa" tool for scanning your repos and orgs.

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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