Ship AI like it's any other software.
We design MLOps pipelines that turn AI development into a repeatable engineering practice, versioned datasets, automated training, eval gates, and one click deploys. So your team ships faster and never wonders which model is in production.
The pipeline
your team will trust.
Most ML stacks are duct tape. We build pipelines that are observable, reproducible, and boring in the best way, so your engineers can focus on the model, not the plumbing.
Dataset Versioning
DVC, LakeFS, or your data platform's native versioning, so every training run is tied to an exact, reproducible dataset state.
Training Pipelines
Reproducible training across notebooks, batch jobs, and distributed clusters, orchestrated via Airflow, Argo, Kubeflow, or Prefect.
Experiment Tracking
MLflow, Weights & Biases, or Neptune, every run logged with config, metrics, and artifacts your team can compare in seconds.
Eval Gated Promotion
Models can't reach production without passing your eval suite. Quality gates, regression checks, and approval workflows in CI.
Automated Retraining
Drift triggers and scheduled retrains feeding the same pipeline, with rollback if the new model regresses on key metrics.
Model Registry
One source of truth for model lineage, what data, what code, what version, what's in production. Audit ready by default.
From scratch or on your stack.
We design pipelines around the tools your team already runs, and only introduce new ones when they earn their keep.
Stack Audit
What you already have, orchestrator, registry, data warehouse, and where the rough edges are causing real pain.
Pipeline Design
End to end design, with one new component at a time, validated against your real workloads before going wider.
Build & Backfill
We build the pipeline and backfill historical models into the registry, so you start with a coherent picture, not just future runs.
Embed & Train
We pair with your team for the first few real cycles, then transfer ownership with documentation and on call playbooks.
Questions about
MLOps Pipelines
Often just better tooling. We start with the highest friction part of your current workflow, usually eval or deploy, and only add platform pieces when the ROI is clear.
Airflow, Argo, Prefect, Kubeflow, Dagster, Metaflow for orchestration. MLflow, W&B, Neptune for tracking. SageMaker, Vertex, Databricks where they make sense. We're tool agnostic, we follow your team.
Same principles, different artifacts. Prompt versioning, eval suites, dataset curation, and inference cost tracking become the heart of the pipeline instead of training loops.
The first real pipeline shipping a model end to end takes 4/8 weeks. Maturity across the org takes longer, but you get value from the first pipeline immediately.
Yes, that's the goal. We pair program, document, and run the first few cycles together. By the end your team owns the pipeline and we're optional.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Tell us where your pipeline breaks today, we'll diagnose where the leverage actually is.
info@croncore.com