> For the complete documentation index, see [llms.txt](https://playbooks.equalexperts.com/mlops-playbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://playbooks.equalexperts.com/mlops-playbook/principles/apply-continuous-delivery.md).

# Apply continuous delivery

## <mark style="color:blue;">Machine learning solutions are complex software and should use best practice</mark>

We want to be able to amend how our machine learning models consume data and integrate with other business systems in an agile fashion as the data environment, downstream IT services and needs of the business change. Just like any piece of working software, continuous delivery practices should be adopted in machine learning to enable regular updates of those integrations in production. Teams should adopt typical continuous delivery techniques, use Continuous Integration and Deployment (CI/CD) approaches; utilise Infrastructure as Code (Terraform, ansible, packer, etc.) and work in small batches to have fast and reasonable feedback, which is key to keeping a continuous improvement mindset.


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