Community Discussions

Fast experiment loops matter. How quickly can you go from a new dataset to a deployed adapter on [Predibase](https://saaskart.co/software/predibase)?

At high volume, per-token API pricing hurts. How did serving a fine-tune on [Predibase](https://saaskart.co/software/predibase) compare on total cost for your w

Small fine-tuned models are great at extraction. How is [Predibase](https://saaskart.co/software/predibase) performing on JSON or entity extraction versus a lar

Fine-tuning means sending training data somewhere. What controls does [Predibase](https://saaskart.co/software/predibase) offer, and are teams using VPC deploym

After fine-tuning on [Predibase](https://saaskart.co/software/predibase), how are you measuring whether the new model is actually better on your real distributi

Llama, Mistral, and others are all options on [Predibase](https://saaskart.co/software/predibase). How are you picking a base model for a given task and budget?

You can fine-tune with raw Hugging Face and PEFT. What did [Predibase](https://saaskart.co/software/predibase) save you versus rolling your own training and ser

The multi-adapter serving in [Predibase](https://saaskart.co/software/predibase) promises big savings. How many adapters are people packing onto one base model

A common blocker is dataset size. For tasks fine-tuned on [Predibase](https://saaskart.co/software/predibase), how many examples did it take to beat a prompted

Prompt engineering a large API model is easy, but [Predibase](https://saaskart.co/software/predibase) argues fine-tuning a small model wins on cost and latency.

Mixed workloads on [Anyscale](https://saaskart.co/software/anyscale) need a smart node mix. How are you splitting GPU and CPU node pools to avoid paying for idl

[Anyscale](https://saaskart.co/software/anyscale) supports Ray Serve for online inference. How does it compare to dedicated inference platforms for latency and

If you already run Ray locally, how much work is it to move to [Anyscale](https://saaskart.co/software/anyscale)? Any config or dependency surprises?

Some teams run ML on Kubernetes directly. How does [Anyscale](https://saaskart.co/software/anyscale) compare for developer experience and scaling versus a K8s-b

When a Ray job fails across dozens of nodes, root-causing is painful. What tools and dashboards in [Anyscale](https://saaskart.co/software/anyscale) actually he

Distributed compute bills add up fast. What quotas, autoscaling limits, and monitoring do you set on [Anyscale](https://saaskart.co/software/anyscale) to keep s

Data residency often requires compute in your own VPC. How smooth is the [Anyscale](https://saaskart.co/software/anyscale) setup for running clusters inside you

Fine-tuning large models across many GPUs is hard to orchestrate. How has [Anyscale](https://saaskart.co/software/anyscale) with Ray Train worked for your multi

Batch scoring millions of records is a classic Ray use case. How are people structuring Ray Data pipelines on [Anyscale](https://saaskart.co/software/anyscale)

Open-source Ray is free, so when is [Anyscale](https://saaskart.co/software/anyscale) worth paying for? For teams that upgraded, what enterprise features tipped