Scalable ML Evaluation on Databricks

[vkamma]

Hi everyone,

I recently worked on designing a scalable machine learning evaluation pipeline using Databricks. The goal was to automate model scoring and analysis across large datasets while maintaining version control, metric tracking, and reproducibility.

Databricks’ distributed compute environment, along with its strong support for orchestration and experimentation, helped us streamline our end-to-end evaluation workflow. Key focus areas included:

• Parallelizing model inference for high throughput
• Tracking evaluation metrics and runs consistently
• Managing dataset and result versioning with ease

If you’re exploring similar use cases or have insights on optimizing ML evaluation frameworks in Databricks, I’d love to hear from you. Open to sharing technical details and learning from others in the community.

[ShloWadh_71379]

Hey Vkamma, 

Thanks for sharing your experience—this sounds like a solid approach to scaling model evaluation with Databricks. I’m especially curious about how you managed versioning for both datasets and results—did you leverage Delta Lake, MLflow, or something custom?

A few other questions that come to mind:

• How did you handle model drift or data distribution shifts over time? Was there a retraining trigger in your pipeline?
• Were there any performance bottlenecks you had to work around when scaling inference?
• Did you evaluate multiple models in parallel, or was the focus on a single model’s evolution?

Would love to hear more about your orchestration layer too—did you rely on Databricks Workflows, or did you integrate with tools like Airflow?

Always great to learn from others tackling real-world ML ops challenges!