Dibs deploys your models, watches them in production, and retrains them when they drift. Just tell it what you need, in plain English.
The model is the easy part. Everything around it is where teams stall.
Data scientists hand off code that engineers rewrite for weeks.
Data drifts and accuracy drops, and nobody notices until customers do.
Tracking, registry, serving and monitoring are separate tools, and all of them are fragile.
Idle clusters and over-provisioned endpoints quietly burn budget.
Think of it as the MLOps engineer you don't have to hire.
"Deploy churn-v3 to staging." Dibs builds the container, sets up autoscaling and gives you an endpoint.
Latency, drift, data quality and accuracy, with alerts that explain the cause and not just the symptom.
Dibs triggers retraining on drift, compares candidates and does a canary rollout, with your approval.
Ask "why did predictions change on Tuesday?" and get a traced answer from data, code and config.
Right-sizes instances, scales to zero and flags idle endpoints with per-model cost attribution.
Every action is logged, gated by roles and reversible. Dibs asks before anything risky.
Works with the stack you already have. No rewrite and no lock-in.
Link your repo, cloud and model registry with read-only access to start.
Chat in the web app, Slack or your terminal. Dibs plans the work and shows you what it will do.
You review, approve and Dibs executes, then keeps watching after launch.
Not on its own. Risky actions need your approval, and every change is logged and can be rolled back.
It stays in your cloud. Dibs runs in your VPC, and a hosted option is available.
Yes: serving, prompt versioning, evaluation and cost tracking.
The beta is free for early users. Pricing is usage-based after launch.