Applying continuous delivery in machine learning (CD4ML) projects is hard, for a few reasons:
- Two worlds (software and data) have collided in recent years, and it takes time and experience for data practitioners to adopt continuous delivery principles and practices (and vice versa!)
- Data tools and platforms are shipped so quickly by cloud providers, and they often focus on storage and compute, leaving CI/CD practices (e.g. unit testing, test data management) as second-class considerations to be figured out by teams
- It’s easy to choose a tool or platform, and find ourselves locked in and limited by the tool’s API
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In our experience, we useÌýÌýas a north star to help us navigate through this chaotic environment. Instead of looking for a single data platform as silver bullet, we’ve had greater success by:
- Composing implementations from first principles (such as automated testing, shifting quality left, post-deployment monitoring, etc.)
- Preferring composition over monolithic platforms
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If you’re interested in discussing how we could help you on this journey, or want to chat about how you’re tackling them, we’d love to hear from you!
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Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of ºÚÁÏÃÅ.
