Building a machine learning (ML) model is both fascinating and complex, requiring careful navigation through a series of steps. The journey from machine learning model development to deployment is the most critical phase in bringing AI to life. A well-trained model, on the right algorithm and relevant data, covers the development stage, then the focus shifts toward deployment.
Deploying a machine learning model can be a tedious process: building APIs, containerizing, managing dependencies, configuring cloud environments, and setting up servers and clusters often require significant effort, but imagine if the entire workflow could be automated. In this article, we’ll talk about how ML deployment automation can unify and simplify all these processes. The deployment process can be simplified by using general tools, preconfigured modules, and easy-to-integrate automated scripts.