Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives. Though gaining worldwide acclaim, ML models are hard to be translated into active business gains. A plethora of engineering, data, and business concerns become bottlenecks while handling live data and putting ML models into production.

Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives. Though gaining worldwide acclaim, ML models are hard to be translated into active business gains. A plethora of engineering, data, and business concerns become bottlenecks while handling live data and putting ML models into production.

There are alternatives to Docker Swarm for container orchestration, namely Kubernetes, which is also arguably the more popular option. There are also cloud-based solutions for deploying machine learning models (e.g. However there are times where we may want to roll our own solution, whether it is for learning or for self-hosting the entire pipeline (i.e., not relying on the cloud). The latter being quite common in government or regulated industries. In my job where data scientists and engineers often need to deploy within various sensitive and air-gapped networks that are "infra-constrained" (e.g., one cannot expect an on-premise Kubernetes cluster raring to go), we had to be self-sufficient in deploying and scaling ML models in a variety of constrained environments. Docker Swarm is my preferred choice in these admittedly niche scenarios.

The workflow for building machine learning models often ends at the evaluation stage: you have achieved an acceptable accuracy, and "ta-da! Beyond that, it might just be sufficient to get those nice-looking graphs for your paper or for your internal documentation. In fact, going the extra mile to put your model into production is not always needed. And even when it is, this task is delegated to a system administrator. However, nowadays, many researchers/engineers find themselves responsible for handling the complete flow from conceiving the models to serving them to the outside world.