Developed comprehensive deployment and configuration documentation for a machine learning engine focused on anomaly correlation within the HyperSoftLab/docs repository. The work detailed a repeatable workflow for deploying the ML engine using Docker containers and configuring Nginx as a reverse proxy, providing clear, step-by-step guidance for engineering teams. By leveraging Bash scripting, YAML configuration, and Markdown for documentation, the contribution streamlined onboarding and reduced deployment errors for ML features. The documentation was co-authored in collaboration with other team members, emphasizing cross-team knowledge sharing and best practices. No bugs were reported or fixed during this period, with efforts concentrated on feature delivery.