Automated Workflow Implementation for ML Ops

📅 2024-04-09 — Session: Automated Workflow Implementation for ML Ops

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

Session Goal:

The goal of this session was to explore and implement automated workflows in the context of Machine Learning Operations (ML Ops) using GitHub Actions and CI/CD pipelines.

Key Activities:

• Developed a detailed strategy for automating updates in ML Ops, covering data detection, model retraining, and updates.
• Implemented CI/CD systems with GitHub Actions for data automation, including YAML workflow examples.
• Configured GitHub Actions workflows for microdata updates, including scheduling, dependencies, and testing.
• Designed robust ML Ops processes with a focus on modularization, automation, and documentation.
• Proposed improvements for ML Ops scripts, focusing on modularization, error handling, and model evaluation.
• Created workflows in GitHub Actions for ML Ops projects, automating data preprocessing and model updates.
• Explained the use of __init__.py in Python projects for better code structure and module imports.
• Provided a step-by-step guide for reconciling Git changes and cleaning repositories with updated .gitignore files.

Achievements:

• Successfully outlined and partially implemented automated workflows for ML Ops using GitHub Actions.
• Enhanced understanding of modularization and automation in ML Ops processes.

Pending Tasks:

• Complete the implementation of CI/CD pipelines for all identified workflows.
• Further test and refine the GitHub Actions workflows for reliability and efficiency.
• Continue improving ML Ops scripts based on proposed enhancements.