Automated CI/CD Implementation for ML Ops

📅 2024-04-09 — Session: Automated CI/CD Implementation for ML Ops

🕒 00:00–01:40
🏷️ Labels: CI/CD, Github Actions, Ml Ops, Automation, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective was to establish a robust CI/CD pipeline for ML Ops using GitHub Actions, focusing on automating data updates, model retraining, and workflow orchestration.

Key Activities

• Automatización de Actualizaciones en ML Ops: Developed a strategy for automated workflow management in ML Ops, covering data detection to model updates.
• Implementación de CI/CD con GitHub Actions: Implemented CI/CD using GitHub Actions to automate data preprocessing and model retraining.
• Automatización de Actualización de Microdatos: Configured GitHub Actions to automate microdata updates, including scheduling and dependency management.
• Diseño de Procesos Robustos en ML Ops: Planned a modular approach for ML Ops processes to enhance scalability and collaboration.
• Mejoras en Scripts de ML Ops: Proposed improvements for ML Ops scripts focusing on modularity and error handling.
• Implementación de Workflows en GitHub Actions: Detailed the creation of workflows for data preprocessing and model updates in ML Ops.
• Uso de __init__.py en Proyectos de Python: Explained the use of __init__.py for better project structure in Python.
• Reconciliación de Cambios en Git: Provided a guide for synchronizing local and remote Git repositories.
• Limpiar Repositorio de Git y Actualizar .gitignore: Outlined steps to clean Git repositories and update .gitignore files.

Achievements

• Established a comprehensive CI/CD pipeline using GitHub Actions for ML Ops.
• Enhanced script modularity and error handling in ML Ops projects.
• Improved Git repository management and synchronization.

Pending Tasks

• Further testing of the CI/CD workflows to ensure reliability and efficiency.
• Documentation of the implemented workflows for team collaboration.