Machine learning and DevSecOps: Inside the OctoML/GitLab integration

This post details the integration of OctoML CLI with GitLab CI/CD pipelines to automate the optimization and deployment of machine learning models. It describes how new commits to inference code repositories can trigger OctoML CLI to optimize models for cost and latency, deploy them to a cloud registry, and then to managed Kubernetes services. The post outlines the four required stages for every pipeline (setup, package, deploy, test) and provides an example of the `octoml.yaml` configuration for model acceleration.