# CI/CD for Machine Learning
CI/CD is a software design concept that refers to the combined practices of continuous integration and continuous deployment of applications. CI refers to building an application and CD refers to deploying it. The concept implies that there is automation around these processes.
**How is this relevant to ML?** At a high-level, CI/CD as a great analogy for how ML teams will develop and deploy models in the future. The current system is messy and ad hoc with no standards or best practices in place. By applying CI/CD methodology, ML teams can be more streamlined and scientific in their approach.
## CI/CD for Machine Learning + Gradient
Gradient pioneered the concept of CI/CD for machine learning. The platform is designed to provide this functionality out of the box with [GradientCI](https://gradient.paperspace.com/gradientci) and an [SDK](https://docs.paperspace.com/gradient/gradient-python-sdk/gradient-python-sdk) for constructing advanced pipelines. Here’s a more in-depth blog post on the topic:
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