Infrastructure for Machine Learning Applications in Go Workshop

Description

At the heart of Machine Learning is the model’s code, but it only accounts for a fraction of the overall code of a production system and the rest is the infrastructure needed to power and utilize it. In this workshop, we will focus on the infrastructure of a Machine Learning application written in Go and integrate the systems needed to deploy a ML model at scale.

What a student is expected to learn

How to robustly build a Machine Learning system and how to build a service that interfaces with a fast secondary data source to enhance the model’s predictions. We will look at infrastructure, model serving/deployment and data enrichment separately and create a working stack together.

Prerequisites

You will need to have some familiarity with Go. Some familiarity with Machine Learning can be helpful but is not required, as we will cover the basic concepts of what is a Machine Learning model, what is training, what is inference, etc.

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