The full MLOps capability library

The operational backbone that gets models to production and keeps them healthy — without it only ~53% of AI prototypes ever reach production (Gartner).

techniques

disciplines

capabilities

the whole spectrum

manual → autonomous · not just the fashionable end

18 manual39 automated35 autonomous

/ browse

The 6 capabilities, in depth

Pick a capability — its plain-English job, the questions it answers, and the full toolbox scroll alongside.

the colour is the tiermanualautomatedautonomous

Ship

from notebook to production, safely

16techniques · 2 disciplines

3 manual7 automated6 autonomous

Turn a one-off model into a repeatable, tested pipeline that builds, validates and deploys itself — so a new version reaches production in hours, and rolls back in seconds if it misbehaves.

answers questions like

→“How long does it take us to get a model change safely into production?”
→“If a new model version is worse, how fast can we roll it back?”
→“Can we retrain automatically when the world changes, without a person babysitting it?”

the toolbox — 16 techniques, manual to autonomous

Pipelines

9 techniques

Manual deployHand-run scriptsCI/CD (GitHub Actions / GitLab CI)Pipeline orchestration (Kubeflow Pipelines / Argo Workflows)Model-validation gatesDocker / OCI packagingContinuous training (CT)Drift-triggered retrainingGitOps (Argo CD / Flux)

Deployment

7 techniques

Big-bang / manual cutoverCanary releaseBlue-green deploymentShadow / mirror deploymentChampion-challengerProgressive-delivery rollout (Argo Rollouts / Flagger)Automated rollback on metric regression

/ the point

A model in a notebook earns nothing

Production, monitoring and fast recovery are where value is realised — so we run the loop that keeps your models live, honest and cheap, not just the demo that trained well.

Back to the service

the models themselves with Data Science & ML · the data pipelines with AI Data Infrastructure · LLM apps with Generative AI