Ship Models Like You Ship Code

CI/CD for ML, experiment tracking, model registry, and deployment automation — the operational foundation that makes every future model release faster and safer.

Duration: 5–7 days Team: 1 Senior ML Architect + 1 MLOps Engineer

The Challenge

You might be experiencing...

Deploying a new model version takes 2 days of manual work — stopping training, exporting weights, updating configs, SSHing into servers — and every deployment is slightly different from the last.

You have no experiment tracking. Your team runs experiments in notebooks, saves results to a shared spreadsheet, and regularly can't reproduce a result from three weeks ago.

Your model registry is a naming convention in an S3 bucket. You have no metadata, no versioning, no lineage — just files with timestamps in the filename.

You want automated retraining when your data distribution shifts, but right now retraining is a manual process that requires a senior engineer to babysit.

The MLOps Foundation Sprint bridges the gap between a working ML model and a production ML system — by designing the operational infrastructure that makes model deployment as reliable and repeatable as software deployment.

The MLOps Gap

Most ML teams reach a point where the model works but the operations don’t. The model is accurate. The serving latency is acceptable. But:

Deploying a new version takes a day of manual work and a senior engineer
Nobody can reproduce the experiment that produced the best model from last month
The model registry is a folder with files named model_v2_final_final_really_final.pkl
Retraining is a manual process that requires someone to remember the right sequence of commands

These are not model problems. They are MLOps problems — and they compound. Every manual deployment is a deployment that might not happen. Every non-reproducible experiment is engineering work that might get repeated. Every undocumented rollback is an incident waiting to happen.

What the Foundation Sprint Builds

CI/CD for ML works differently from CI/CD for software. The pipeline needs to handle training jobs, not just builds — with data versioning, experiment logging, model validation gates, and deployment approval workflows. The sprint designs this pipeline end to end, with the specific tooling choices documented and justified.

Experiment tracking is the difference between an ML team that learns and one that runs in circles. The setup guide we deliver includes a metadata schema that makes experiments comparable — not just logged — and naming conventions that scale as your team grows.

Model registry architecture solves the versioning problem that every ML team hits: how do you track which model version is in which environment, what data it was trained on, and what its performance characteristics are? The registry design we deliver answers these questions with a structured metadata schema and lineage tracking.

The Handoff

The sprint delivers documentation, not just diagrams. Every design includes implementation guidance specific to your stack, so your engineering team can build from the deliverables without coming back to us for clarification.

Our Approach

Engagement Phases

Days 1–2

MLOps Audit & Tooling Selection

Review of your current model deployment process, experiment tracking practices, and infrastructure setup. We map the gaps against a production MLOps reference architecture and produce a tooling recommendation — experiment tracking, model registry, CI/CD platform, and orchestration — based on your stack, team size, and scale requirements.

Days 3–5

Pipeline Architecture Design

Design of the full MLOps pipeline: CI/CD workflow for model training and deployment, experiment tracking setup with metadata schema, model registry architecture with versioning and lineage, and packaging/containerisation standards. Every design decision is documented with rationale and implementation guidance.

Days 6–7

Deployment Architecture & Handoff

Design of the deployment pipeline — staging environments, canary deployment strategy, health checks, and rollback triggers. Delivery of the full documentation package: pipeline designs, setup guides, deployment runbook, and rollback procedures. Includes a 60-minute handoff session with your engineering team.

What You Get

Deliverables

ML CI/CD Pipeline Design — workflow diagram with trigger conditions, stages, and approval gates

Experiment Tracking Setup Guide — schema design, naming conventions, and integration patterns

Model Registry Architecture — versioning strategy, metadata schema, and lineage tracking design

Deployment Pipeline Blueprint — staging strategy, canary rollout design, health check specifications

Rollback Runbook — step-by-step procedures for safe model rollback with decision criteria

Expected Outcomes

Before & After

Metric	Before	After
Model Deployment Frequency	2-day manual deployment process — 1–2 deployments per month maximum	Automated CI/CD pipeline — same-day deployments with full audit trail
Experiment Reproducibility	No experiment tracking — results stored in spreadsheets, 30% of experiments non-reproducible	Every experiment logged with parameters, metrics, and artefacts — 100% reproducible
Rollback Time	Rollback requires senior engineer intervention — 4–6 hours under pressure	Documented rollback runbook — any engineer can execute in under 30 minutes

Technology

Tools We Use

MLflow / Weights & Biases DVC GitHub Actions / ArgoCD Docker / Kubernetes

Common Questions

Frequently Asked Questions

Which MLOps tools do you recommend and why?

Our recommendations depend on your team size, existing infrastructure, and scale requirements. For experiment tracking, MLflow is the default for self-hosted teams with existing infrastructure; Weights & Biases is preferred for teams that want a managed solution and have the budget. For CI/CD, GitHub Actions covers most cases for model training pipelines; ArgoCD is recommended when you are already running Kubernetes and want GitOps-style deployment. We document the tradeoffs and make a specific recommendation based on your constraints.

Do we need Kubernetes to implement this?

No. The MLOps Foundation Sprint is designed to work at your current infrastructure level. If you are running on bare EC2 instances, we design a pipeline that works with that setup and includes a clear migration path to containers and orchestration when you are ready. We do not recommend Kubernetes as a prerequisite — it is a valid target state for later, not a Day 1 requirement.

How long does implementation take after the sprint?

The sprint delivers design documentation and setup guides, not a running implementation. Implementation timeline depends on your team's capacity and the complexity of your existing stack. Most teams implement the core CI/CD pipeline and experiment tracking in 2–3 weeks following the sprint. The design documentation is structured to make implementation self-contained — a mid-level ML engineer can execute it without ongoing support.

What if we already have some MLOps tooling in place?

We audit what you have on Days 1–2 and design around it. If your existing tooling is appropriate for your scale, we integrate it into the pipeline design. If it has gaps or limitations, we document the tradeoffs and give you an upgrade path. We do not recommend replacing working tools without a clear justification.

Build ML that scales.

Book a free 30-minute ML architecture scope call with our experts. We review your stack and tell you exactly what to fix before it breaks at scale.

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