Introduction¶
Learn how to collaborate online using Git, enable seamless integration with a CI/CD pipeline and visualize reports with CML.
Requirements¶
The following requirements are necessary to follow this part in addition to those described in the first part:
- A GitHub account
- A Google Cloud account
Using different platforms? Read this!
While this guide uses GitHub and Google Cloud for examples, the core MLOps principles and architecture patterns apply to other platforms with some adjustments:
Note
A credit card might be necessary to use cloud services.
Before proceeding with this section, please ensure that you have a valid payment method, as it may be required to utilize cloud services. It is important to note that at the conclusion of this section, you will need to delete the cloud resources that were created to avoid any potential charges.
While the costs associated with this section are expected to be free, it is recommended to review the pricing details of cloud services before initiating this part.
State of the MLOps process¶
Now that the experiment runs locally, the next step is to make it collaborative and reproducible in the cloud. In this part, you will address the following issues:
- Codebase requires manual download and setup
- Dataset requires manual download and placement
- Experiment may not be reproducible on other machines
- CI/CD pipeline does not report the results of the experiment
- Changes to model are not thoroughly reviewed and discussed before integration