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Automatic Training, Testing, and Deployment of AI using CI/CD

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Automatic Training, Testing, and Deployment of AI Using MLOps: Overview

In this article series, we'll demonstrate how to take use a CI/CD pipeline - a tool usually used by developers and DevOps teams - and demonstrate how to use it to create a complete training, test, and deployment pipeline for AI that meets the requirements of level 2 in the Google MLOps Maturity

Added 4 May 2021
1

Setting Up GitHub, Docker, and Google Cloud Platform for Automated MLOps

In this article, we set up a cloud environment for this project.

Added 4 May 2021
2

Model Auto-Adjustment in an MLOps Pipeline

In this article, we’ll implement automatic training.

Added 5 May 2021
3

Continuous Training in an MLOps Pipeline

In this article, we’ll deep-dive into the Continuous Training code.

Added 6 May 2021
4

MLOps Continuous Delivery with Model Unit Testing

In this article, we develop a model unit testing container.

Added 7 May 2021
5

Building an MLOps Model API

In this article we build the model API to support the prediction service.

Added 10 May 2021