From Notebook to Production: How We Deploy ML Models in 8 Weeks
The Notebook Trap
We see this pattern constantly: a data science team spends months building a fraud detection model, a recommendation engine, or a forecasting system. It performs well in Jupyter notebooks. Everyone's excited. Then the handoff to engineering happens, and everything stalls. The engineering team says they need 3-6 months to 'properly containerize and deploy it.' Six months later, the model is still sitting in a notebook, and the business is still waiting.
Why It Takes So Long (And Shouldn't)
The bottleneck isn't the model. It's the infrastructure around it. Most teams don't have standardized ML deployment pipelines. Every model becomes a bespoke project: custom Docker configurations, one-off Kubernetes manifests, manual testing processes, and ad-hoc monitoring. Each deployment reinvents the wheel. The fix isn't hiring more engineers. It's building a repeatable pipeline that works for every model.
The Pipeline We Build
Our approach is straightforward. Week 1-2: We containerize the model with standardized Docker images that handle dependency management, GPU allocation, and health checks. Week 3-4: We set up model versioning and a registry so you can track every iteration, roll back instantly, and compare performance across versions. Week 5-6: We build the A/B testing framework so you can safely test new models against production baselines with real traffic. Week 7-8: We deploy to Kubernetes with auto-scaling, monitoring, and alerting. The model scales with demand and you get paged when something actually matters.
What Changes After
Once the pipeline is in place, deploying the next model takes hours, not months. Your data scientists push code, the pipeline handles the rest. One client went from quarterly model updates to weekly. Their uptime went to 99.5% with auto-failover. And the engineering team stopped dreading ML deployments because they became boring, which is exactly what deployments should be.
The Real Cost of Waiting
Every month a model sits in a notebook is a month of value you're not capturing. If that fraud detection model catches $200K in fraud per month once deployed, a 6-month delay costs $1.2M. The infrastructure investment pays for itself in the first sprint.