Case studies

MLCommons — MLOps & benchmarking

Large-scale machine learning benchmarking with reliable MLOps engineering—not ad-hoc experimentation.

MLCommons engaged ADVOP when they were working on large-scale machine learning benchmarking initiatives and needed reliable MLOps engineering support rather than ad-hoc experimentation.

We focused on workflows that make benchmarking and grouping across GPU configurations repeatable: pipelines you can trust in production, with operational ownership for the ML infrastructure underneath.

Scope & outcomes

Build and manage MLOps workflows
Enable benchmarking and grouping of different GPU configurations
Create repeatable, reliable ML evaluation pipelines
System engineering and operational ownership for ML infrastructure

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