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MLOps Engineering & Coding Best Practices with Maria Vechtomova #48
May 30, 2024
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.
59:51
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Quick takeaways
- Implementing traceability and good coding practices enhances the reliability of machine learning models and their deployment processes.
- Understanding the roles of ML engineers versus MLOps engineers fosters organizational efficiency and facilitates smoother operational workflows for machine learning projects.
Deep dives
Importance of Code Practices in Data Science
Traceability and reproducibility are crucial practices for data scientists working with machine learning models. It is essential to maintain clear records of which code, data, and run were responsible for model outcomes, similar to the discipline required in exercising where consistency leads to results. Adopting good coding practices, such as modularizing code and writing tests, contributes to better model deployment and maintenance. By treating coding with the same rigor as other professional tasks, data scientists enhance the reliability of their work.
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