![MLOps workflow with Github Actions [Video]](https://pixhost.icu/avaxhome/8a/55/0081558a_medium.jpg)
MLOps workflow with Github Actions [Video]
![MLOps workflow with Github Actions [Video]](https://pixhost.icu/avaxhome/8a/55/0081558a.jpg)
MLOps workflow with Github Actions
English | 2021 | h264, yuv420p, 1280x720 | 44100 Hz, 2channels | Duration: 1h 5m | 286 MB
English | 2021 | h264, yuv420p, 1280x720 | 44100 Hz, 2channels | Duration: 1h 5m | 286 MB
Get started with MLOps and Github Actions to package a container with an ONNX model that does live inferencing with a Flask application. By using Azure ML, learn how to download the large ONNX model into the Github Action workflow, package it as a container and then push it to a container registry. For reference use the https://github.com/alfredodeza/flask-roberta repository
Topics include:
* Create a container that does live inferencing with Flask and the ONNX runtime
* Package the model and verify it works locally
* Setup a Github Action to authenticate to Azure ML and download a previously registered model
* Build the new container as a Github Action, authenticate to Docker Hub or Github Packages
* Push the new container to the Github registry or any other registry like Docker Hub