Uploaded March 2021 | Updated September 2026, 2 weeks ago
In this video tutorial I demonstrate how to track Machine Learning or Deep Learning metrics across different Github branches. For example, if you have a master or main branch in your Github repository, and want to make some changes in your model/Python code without touching the main branch, the solution is provided in this video.
For this, we will use Continuous Machine Learning (CML) technique which is very popular and mostly used in MLOps (Machine Learning Operations) domain, and Github Actions which becomes more and more popular among Data Scientists and Developers (I am Python Developer).
For this purpose we will be using:
- GitHub Actions: github.com/features/actions
- CML: github.com/iterative/cml
- Github Repository: github.com/vb100/mlops-track-perf-branches
As we have discussed in previous MLOps tutorial (youtu.be/9I8X-3HIErc) CI-CD functionality delivered by DVC (Data Versioning Control) can bring the beneficial into this kind of tasks.
This combination of tools let you build your custom ML Pipeline which will run inside the Docker container within the Github Environment, where you can see the progress of all stages in your ML pipeline in real time.
So this tutorial shows the idea how you can to compare different metrics of your Data Science project across multiple Github branches. You can have as many branches as you want. The output of your ML pipeline should be some kind of metrics.json and any format additional files which you can to integrate into Pull requests comments as markdown Readme file very easily.
The content of the video:
0:00 - Main Idea
2:23 - #1. Machine Learning Pipeline for this project
3:13 - #2. Run ML Pipeline Locally
6:48 - #3. Install DVC Package
7:31 - #4. Set ML Pipeline Stages in terminal and YAML file
18:22 - #5. Push DVC to Github Repository
19:37 - #6. Build a Github Action to run ML Pipeline
25:58 - #7. Make changes in ML project code
27:48 - #8. Track changes in metrics across different Github branches
#MLOps #githubactions #cml
In this video tutorial I demonstrate how to track Machine Learning or Deep Learning metrics across different Github branches. For example, if you have a master or main branch in your Github repository, and want to make some changes in your model/Python code without touching the main branch, the solution is provided in this video.
For this, we will use Continuous Machine Learning (CML) technique which is very popular and mostly used in MLOps (Machine Learning Operations) domain, and Github Actions which becomes more and more popular among Data Scientists and Developers (I am Python Developer).
For this purpose we will be using:
- GitHub Actions: github.com/features/actions
- CML: github.com/iterative/cml
- Github Repository: github.com/vb100/mlops-track-perf-branches
As we have discussed in previous MLOps tutorial (youtu.be/9I8X-3HIErc) CI-CD functionality delivered by DVC (Data Versioning Control) can bring the beneficial into this kind of tasks.
This combination of tools let you build your custom ML Pipeline which will run inside the Docker container within the Github Environment, where you can see the progress of all stages in your ML pipeline in real time.
So this tutorial shows the idea how you can to compare different metrics of your Data Science project across multiple Github branches. You can have as many branches as you want. The output of your ML pipeline should be some kind of metrics.json and any format additional files which you can to integrate into Pull requests comments as markdown Readme file very easily.
The content of the video:
0:00 - Main Idea
2:23 - #1. Machine Learning Pipeline for this project
3:13 - #2. Run ML Pipeline Locally
6:48 - #3. Install DVC Package
7:31 - #4. Set ML Pipeline Stages in terminal and YAML file
18:22 - #5. Push DVC to Github Repository
19:37 - #6. Build a Github Action to run ML Pipeline
25:58 - #7. Make changes in ML project code
27:48 - #8. Track changes in metrics across different Github branches
#MLOps #githubactions #cml
