Executable tutorial proposal: Deploying a Pre-trained Semantic Search Model as an API with BentoML

contributions/executable-tutorial/emcir-fragale/README.md

@@ -0,0 +1,41 @@
+# Assignment Proposal
+
+## Title
+
+Deploying a Pre-trained Semantic Search Model as an API with BentoML
+
+## Names and KTH ID
+
+- Ettore Mugisha Cirillo (emcir@kth.se)
+- Riccardo Fragale (fragale@kth.se)
+
+## Deadline
+
+- Task 2
+
+## Category
+
+- Executable tutorial
+
+## Description
+
+This executable tutorial uses Google Colab to demonstrate how BentoML turns an existing machine learning model into a service accessible through an HTTP API. It focuses on serving and operating a pre-trained sentence embedding model available on Hugging Face.
+
+The service proposed as example supports a customer support application by finding the FAQ entry most relevant to a user's question. The model converts the question and FAQ entries into embeddings, which the application compares to identify and return the closest match. This scenario provides a concrete use case for sending inference requests to the service.
+
+The tutorial covers defining a BentoML service, calling its API, inspecting the automatically generated API documentation, and building a versioned Bento that packages the service and specifies its dependencies. It also includes checks for correct service responses, readiness, and request metrics.
+
+The tutorial concludes with an explanation of how the packaged Bento could subsequently be deployed in a container environment. This deployment is discussed conceptually; all executable steps take place in Colab without requiring any cloud credentials.
+
+By the end of the tutorial the user should be able to:
+
+- Explain how an existing machine learning model becomes an API service.
+- Define and call a BentoML endpoint.
+- Package the service as a versioned Bento.
+- Perform basic checks of service correctness, readiness, and request metrics.
+
+**Relevance**
+
+Model serving is an important stage of an MLOps workflow. An existing model must be packaged with its code and dependencies, exposed through a stable interface, tested, and monitored. BentoML supports these activities, connecting model serving with DevOps practices such as reproducible packaging, service verification, and observability.
+
+Semantic FAQ search provides just an example through which these practices can be explored.