Maria Vechtomova

Maria Vechtomova, an MLOps Tech Lead and co-founder of Marvelous MLOps, shares her insights on the complexities of MLOps and the advantages of using Databricks. She discusses the challenges data scientists face transitioning from notebooks to production-ready models and stresses the importance of model packaging. The conversation also touches on emerging terms like 'LLM Ops,' new features in MLflow, and the practical uses of Databricks for model serving. Plus, she mentions an upcoming hands-on course and a book on Databricks, aimed at enhancing the learning experience.

Guest Maria Vechtomova is a skilled ML Engineering Manager at Ahold Delhaize and co-founder of the Marvelous MLOps blog. She shares essential coding best practices for data scientists, emphasizing modularity and CI/CD pipelines. Maria discusses her experience deploying a fraud detection algorithm, highlighting the necessity of collaboration and infrastructure monitoring. Additionally, she dives into the distinct roles of ML and MLOps engineers and shares her journey in content creation, offering insights into building a community around MLOps.

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.