Last year, I wrote a blog post on the development and release of Type4Py. Type4Py is a machine learning model for code. In a nutshell, it predicts type annotations for Python source code files and enables developers to add types gradually to their codebases. At the time of the Type4Py release, its deployment was pretty simple. I didn’t use containerization (Docker) and Kubernetes, and the model was deployed on a single machine. There were two clear downsides to the initial deployment approach. First, I could not easily deploy the ML model and its pipeline on another machine. Because I had to install Type4Py and its dependencies on other machines, Second, the ML application could not be scaled well since a single machine’s resources are limited.
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