Maintaining two separate pipelines for ML-powered software systems and conventional software projects can pose challenges. This also introduces friction within the team, which can slow down the development and deployment process.
This article will discuss the cause behind adopting separate pipelines for conventional and ML-powered software projects, the challenges introduced by such construct, and how developers can turn their DevOps pipeline into an MLOps pipeline and become more efficient by embracing ModelKits.
Why can’t we use the same pipeline for ML products and conventional software projects?
Three main reasons necessitate separate pipelines for deploying ML products and conventional software engineering projects:
- Nature of projects
- Individual expertise
- Size and complexity
Side-effects of maintaining two separate pipelines
Peculiarities of ML software have forced companies to keep two teams and maintain separate pipelines to host their traditional software engineering and machine learning projects. This has resulted in:
- Increased cost
- Coordination challenges
- Increased technical debt
Maintaining two separate pipelines for the same functionality can introduce communication overhead, technical debt, and waste company resources. As a result, it is wiser to bind the MLOps and DevOps pipelines into a single unit for efficient deployment of software engineering and machine learning projects. This can be easily achieved by embracing KitOps and ModelKit. Furthermore, compatibility with other open source tools and enterprise support from Jozu make it easier to adopt ModelKit.
To gain a deeper understanding of this topic, check out this article for implementation details with code.
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