MLOPS & MODELS
Your models, managed like infrastructure.
BETAGPU fleet, model registry, training runs, gated promotion, canary serving, drift and cost — on the servers, containers, clusters and storage Talos already manages. Talos records where your weights are and what it observed about them. It never holds the bytes.
THE MANUAL WAY
What running models without a control plane looks like.
THE SPREADSHEET
Which weights are in production, which dataset version produced them, which commit, which run, who approved it. Four people have four answers, and the authoritative one is a tab somebody last edited in March.
THE IDLE ACCELERATOR
Three A100s have been under five per cent for a fortnight, held by a run whose owner left the company. Nothing inventories them, so nothing notices. It is the most boring expensive problem in ML.
THE GREEN TICK THAT MEANT NOTHING
A model was promoted because an evaluation passed. The evaluation ran on different bytes, under a threshold somebody loosened afterwards. The tick was real and it proved nothing.
WHAT TALOS DOES
Six things that stop living in a notebook.
GPU fleet, discovered not declared
A read-only pass over the SSH path that already exists finds every accelerator, its driver, its CUDA version and its UUID — the identity that survives a reseat. It installs nothing.
A registry of pointers, not weights
Talos records an artifact URI and a SHA-256 checksum. Gigabytes of weights never transit the backend and never enter the database — the bytes stay in your storage.
Training runs as tracked jobs
Launch on a GPU server, a Kubernetes Job or a hypervisor VM. Every run records the GPU-hours it consumed and the image digest that makes it reproducible, never a mutable tag.
Promotion gated on the exact artifact
A version cannot enter production unless a required suite passed on that checksum, under the thresholds in force today. Loosening a threshold does not inherit last month’s pass.
Canary on the proxy you already run
Shifting five per cent of inference traffic to a new revision is a weight change on an upstream that already exists. Rollback is one write, not a redeploy.
Cost and idle findings, with their evidence
Observed GPU-hours against an immutable rate history, so a figure quoted last month reproduces today. Every finding carries its window, its row counts and the table each number came from.
HOW IT RUNS
Every action is a tracked job.
WHAT MAKES THIS DIFFERENT
A stage is an instruction. A revision is what is actually deployed.
Promoting a model version to production changes one database column. It deploys nothing, and Talos refuses to let the word imply traffic — only an endpoint revision changes what a user experiences. The same distinction runs through the whole type: a checksum is an observation, a registered version is a claim, and an evaluation bound to that exact checksum is the only thing that turns one into the other. Talos keeps its own ledger so it works with none of MLflow, W&B or Hugging Face installed, and reads from them when they are.
RELATED CAPABILITIES
Point it at one GPU box.
Discovery is read-only. It installs nothing, changes no configuration, and tells you what is actually in the machine.