Pipelines as code
Author ML pipelines as a versioned blueprint and render them to your ML engine.
AI
MLOps
Govern ML pipelines and models like the rest of your delivery
Author machine-learning pipelines as code, render them to engines like Argo and Kubeflow, and ship them through the same gates as application delivery. A model registry holds versions with approval workflows and model cards, so a model reaches production only after a reviewer signs off.
- A model reaches production only after a recorded, human-approved gate decision.
- ML pipelines are governed by the same policy infrastructure as your application delivery.
- Reviewers have one place to evaluate model versions, with lineage and model cards attached.
The problem
You run ML pipelines across one or more engines, but your model review process is informal: approvals happen in chat, model cards live in a spreadsheet, and nothing ties the ML delivery cycle to the same gates that govern your application code. A model can reach production without the documentation or the sign-off your compliance team expects.
Without IntegraCI
- Model approvals tracked in chat and shared documents
- No formal gate before a model reaches production
- ML pipelines governed separately from application delivery
- Provenance and lineage scattered across training tools
With IntegraCI
- A model registry that holds versions, cards, and lineage in one place
- Policy as code gates every candidate before promotion
- ML pipelines ship through the same gate infrastructure as application delivery
- Every model decision recorded in a tamper-evident audit trail
What you get
Model registry
Track model versions, approve or reject them for production, and attach a model card.
In-pipeline gates
A model gate evaluates a candidate against policy before it can be promoted.
Governed and audited
Each model decision is recorded, with AI provenance disclosed alongside it.
How it works
- 1
Author the pipeline
Describe the ML pipeline as code and render it to your engine.
- 2
Register the model
A trained model lands in the registry with its card and lineage.
- 3
Approve and promote
A reviewer approves the model and it promotes through the gate.
How it stays governed
The same gates everyone passes, applied here.
Gated by policy
Each candidate model is evaluated against policy as code before it can be promoted through the gate. The evaluation runs against your defined criteria at promotion time, so a model cannot reach production by bypassing the registry or skipping the gate step.
Recorded, tamper-evident
Each gate decision and model state change writes once to a tamper-evident audit trail, with the evaluation evidence and AI provenance recorded alongside it. You can show what was assessed, who approved it, and when, without reconstructing the record after the fact.
A human in the loop
A reviewer must sign off before a model promotes through the gate. The workflow pauses for human approval rather than advancing automatically, keeping a person in the loop for every state-changing model decision.
Works with your stack
Connect the tools you already run.
Connects to ML pipeline engines and model registries your team already runs, rendering pipeline definitions to those engines and reading model state back through the connector layer.
- Akuity
- Amazon Web Services
- Buildkite
- CircleCI
- CNCF Tekton
- Drone CI
- Harness
- Jenkins
- Alibaba Cloud
- Anthropic
- Argo Workflows
- AsyncAPI Initiative
- Buf
- Cursor
- Databricks
- DataHub Project
- DeepSeek
- Evidently
- +13 more
Who it’s for
Where teams reach for it.
Standardize model review across ML teams
When multiple teams train models on different pipelines, a shared registry and approval workflow gives reviewers one place to evaluate candidates and attach model cards, without requiring teams to change their training stack.
Gate model promotion in regulated environments
If your compliance program requires documented evidence that a model was reviewed before it reached production, the in-pipeline gate and audit trail provide that record without building the process from scratch.
Align ML delivery with application delivery
When your platform and security teams want ML pipelines to meet the same delivery standards as application code, authoring pipelines as code and routing them through the same gate infrastructure makes both consistent.
Questions, answered.
Does IntegraCI replace my ML pipeline engine?
No. IntegraCI renders your pipeline definition to the engine you already run, such as Argo Workflows or Kubeflow. Your engine keeps training and serving; IntegraCI governs what is allowed to promote.
Which engines and registries does IntegraCI connect to?
IntegraCI renders pipelines to engines like Argo and Kubeflow and reads model state from the registries your team already uses. The connector catalog determines which engines are available in your installation.
Who writes the policy that gates a model?
Your team authors gate policy as code. IntegraCI evaluates each candidate model against that policy at promotion time, and the rules are versioned alongside your pipeline definitions.
Can a model promote without a reviewer?
No. A trained model lands in the registry and waits for a human reviewer to approve or reject it. The gate does not promote automatically, and the reviewer's decision is recorded in the audit trail alongside the evidence behind it.
Put MLOps on your stack.
Request a demo, or read the docs to see how it fits the tools you already run.