Deploy Your ML Project

Deploy Your ML Project

Components ready for deployment

Components ready for deployment

What good is your app if no one can use it?

Deploying your machine learning (ML) application from a local environment to a remote server is an important step in making it accessible to users. While it's satisfying to see your app run perfectly on your own machine, the real excitement comes when it's available to the world. In this tutorial, you'll learn how to deploy your application to a remote server.

Tutorial Goals

In this tutorial you will:

  • Upload DVC artefacts to Google Cloud Storage
  • Dockerize your application
  • Deploy to a remote server

By the end of this tutorial, you'll have your ML application up and running on a remote server, ready for users to access. Let's get started!

Setup DVC Remote

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  1. DVC Remote Storage (opens in a new tab)

  2. Docker Getting Started Guide (opens in a new tab)