Machine learning pipelines are increasingly being used by organizations to streamline and scale their ML workflows. However, managing these pipelines can be difficult when an organization has multiple ML projects and pipelines in various stages of development. To address this, Google cloud developer advocates suggested that a method be developed to build on DevOps concepts and apply them to this ML-specific problem.
In this post, Google Cloud developer advocates share some best practices for managing the codebase for your ML pipelines based on their work with top Google Cloud customers and partners.
Click on the link below to read it in detail: