Aimed at Independent Users, this course will take you through the full lifecycle of “model to service”, starting off with an open-source language model and going through the steps to go from a model to a fully usable deployed cloud-hosted application, ready to be integrated into a wider architecture.
This is a practical workshop where you will take a small simple language model and wrap it up in a REST API using FastAPI, containerise it using Docker, deploy it as a cloud-native service and continuously deploy it using GitHub Actions.
Don’t worry if you haven’t used FastAPI, Docker, GitHub Actions or cloud services before – we’ll learn about that as we go.
Learning objectives:
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practical experience wrapping up machine learning models as a simple REST API with FastAPI.
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an understanding of what containerisation is and get practical experience containerising a Python ASGI web application.
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practical experience setting up continuous integration and deployment pipelines using GitHub Actions.
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practical experience spinning up their containerised web application as a cloud-native service.
Pre-requisites
Basic level of programming. Familiarity with the Linux command-line and Python would be useful but not necessary.
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