When most people think about machine learning, their minds immediately go to algorithms such as neural networks, decision trees, ensemble methods, and so on. While these are certainly important, real-world machine learning solutions involve much more than just model selection and training.
One insightful paper illustrates this clearly, highlighting how the majority of effort in ML systems often lies outside the modelling phase. Data pipelines, infrastructure, monitoring, versioning, and deployment play equally, if not more, significant roles in making ML solutions viable in production environments.
Among these, model deployment is a particularly critical stage in the ML lifecycle. Training accurate models is vital, but their value is only realised when they are reliably integrated into production systems and start serving real users at scale.
This article outlines the key deployment strategies and discusses their trade-offs on selecting the right plan for your use case.
Model deployment is the bridge between building a machine learning model and deriving real-world value from it. It’s the stage where your trained model transitions from a development artefact to a production-grade service that interacts with real users or downstream systems.
A robust deployment strategy ensures:
Each deployment strategy comes with its own set of strengths, trade-offs, and implementation complexity. Your choice should align with the specific goals of the system, whether it’s minimising risk, ensuring rapid iteration, or enabling experimentation at scale.
Below are some of the most widely adopted deployment methods in the industry today.
In a canary deployment, a new model version is initially exposed to a small fraction of production traffic, typically 5–10%. If the new version performs well, traffic is incrementally increased until it serves all requests. If regressions are detected, the deployment can be rolled back swiftly.
Canary deployment enables a safe, gradual rollout by exposing the new model to a small fraction of users before full release. Its main advantages are risk mitigation and the ability to quickly roll back if issues arise. However, it demands detailed monitoring to catch regressions early and requires more complex routing logic to manage traffic splits effectively.
In shadow deployment, the new model runs silently alongside the production model, processing the same real-time inputs but without serving its outputs to users. Instead, its predictions are logged and compared with the current production model, enabling safe validation in a live environment.
This strategy offers the advantage of zero user impact while allowing for real-time performance evaluation, making it ideal for de-risking major changes. However, it comes at the cost of additional infrastructure requirements, logging and monitoring setups, and most importantly, doesn’t capture how users might respond to the new model’s outputs, limiting its ability to assess user-facing impact directly.
Blue-green deployment involves running two identical production environments: “blue”, which serves the current stable model, and “green”, which hosts the new version. Once the new model in the green environment has been fully validated, production traffic is switched over in a single step, ensuring a smooth transition.
This strategy enables instant switching with zero downtime, making it particularly suitable for systems with strict uptime requirements. Rollbacks are also straightforward, where they simply route traffic back to the blue environment if any issues emerge post-deployment.
However, the approach comes with the operational cost of running duplicate infrastructure, a big tradeoff when you want to maintain strict SLAs for the whole system.
Selecting the appropriate deployment strategy depends on several key factors:
In practice, organisations often start with simpler approaches such as manual rollouts or basic blue-green setups, and gradually adopt more sophisticated strategies as their infrastructure, team expertise, and monitoring systems mature.
Coming up: In the next articles, I am planning to dive deeper into packaging ML models with Docker and deploying them in real-world environments. Stay tuned for practical guides and hands-on insights.
Model deployment is where machine learning solutions begin to deliver real-world value. By choosing the right deployment strategy and implementing sound engineering practices, organisations can ensure their models are scalable, reliable, and adaptable to change. As ML systems become increasingly complex, adopting robust deployment strategies is no longer just a best practice; it’s a necessity.
What deployment strategy do you use in your ML systems? Let me know in the comments.