Overview
Model deployment involves integrating machine learning models into a production environment where they can provide value to users. Without deployment, a machine learning model cannot be used for decision-making, predictions, or insights. Model deployments can be a challenge for data scientists, requiring the right set of machine learning model deployment frameworks, tools, and processes. The goal of model deployment is ultimately portability, so it can be transferred from one machine learning system to another, and scalability, so a trained model doesn’t require redesign. At C&F, we have a wealth of experience deploying machine learning models. Our solutions include automating the model deployment pipeline, continuous monitoring and logging, and prioritizing scalability and reliability.
