Last updated: February 1, 2023
Introduction
In this project, we show how ZenML empowers users to build, track and deploy a computer vision pipeline using the most popular tools in the industry. You will learn how to train a YOLOv5 model on Vertex AI, track experiments with MLflow, and deploy a BentoML bundle to the Vertex AI endpoint.
We will construct a pipeline for this project which loads the data, trains a model, and trains a model remotely in VertexAI.
Use case
This project can be used anywhere you need to create a pipeline to train object detection models and deploy them at scale on Vertex AI.
Stack and Components
This project uses the following Stack Components:
- Orchestrator - Local Orchestrator.
- Artifact Store - Google Cloud Storage.
- Container Registry - Google Container Registry.
- Step Operator - VertexAI.
- Experiment Tracker - MLflow.
Run
Code
The codes to reproduce this project are open-source ZenML Project repository on GitHub. View the code here.
