# Scale your VLA pipeline with Anyscale + Weights & Biases

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## Email content

Hi Thomas,

A production VLA fine-tuning pipeline has to scale three things across different hardware: data loading on CPUs, training on one class of GPUs, and policy evaluation on another. Add stochastic sim rollouts at scale, and moving from simulation to real-world deployment gets hard to do with confidence.

Join Omar Shorbaji (Solutions Engineering Manager, ML Platforms, Anyscale) and Anushrav Vatsa (Sr. AI Solutions Engineer, Weights & Biases) for a practical look at how to orchestrate VLA pipelines end-to-end with Ray and track it all with W&B Models.

What you'll learn:

- How to orchestrate heterogeneous training + sim-eval pipelines, scaling each stage on the right hardware

- How to make sense of stochastic sim-eval at scale and connect results back to training decisions

- Patterns for tracking sim artifacts (video rollouts, trajectory data, task success distributions) alongside training metrics

- A reusable blueprint you can adapt to your own robotics workflow

Tuesday, May 12, 2026 | 10AM PT / 1PM ET | Virtual

Register

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