MLOps.community MLOps with Databricks // Maria Vechtomova // #314
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May 13, 2025 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.
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Maria's MLOps Journey
- Maria built custom MLOps tools before Databricks existed, gaining deep understanding of combining ML tooling.
- She now prefers Databricks as an integrated platform to avoid the complexity of stitching multiple tools together.
Notebooks Harm MLOps Lifecycle
- Notebooks are popular but harm the MLOps lifecycle due to difficulty transitioning to production.
- Teaching data scientists to package code outside notebooks makes production ML more reliable and manageable.
Use Asset Bundles for Development
- Package ML code for reproducibility instead of using notebooks directly.
- Use Databricks Asset Bundles to define and deploy workflows and development assets efficiently.