Maria Vechtomova, MLOps Tech Lead and co-founder of Marvelous MLOps, shares her insights on the essential differences between MLOps and traditional DevOps roles. She addresses the challenges DevOps engineers face when adopting machine learning workloads. Key discussions include best practices for model accuracy versus computational efficiency and navigating sensitive data in MLOps. Maria also delves into tools like Databricks and AWS SageMaker, and the potential of Golang for CI/CD automation, providing a roadmap for transitioning into MLOps.
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question_answer ANECDOTE
Maria's MLOps Origin Story
Maria started in data as a data analyst focused on econometrics, then transitioned to data science.
She self-developed MLOps tools to deploy machine learning workflows when none existed.
insights INSIGHT
Core Principles of MLOps
MLOps involves many evolving tools but emphasizes principles like traceability and reproducibility.
Monitoring data drift and model changes is crucial beyond just code and artifact versioning.
volunteer_activism ADVICE
Transitioning from DevOps to MLOps
DevOps engineers moving to MLOps should learn basic data science concepts and how data scientists work.
Bridging the gap requires understanding data scientists' workflows and improving best coding and security practices.
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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.
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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.