Automation and Insights in Projects

Intermediate GitHub Concepts

Arne Jonas Warnke

Content Lead for ML- and Data Engineering at DataCamp

Importance of Automation and Insights

GitHub Actions:

🔄 Streamlines: Automates workflows
🚀 Automation: Simplifies processes

Insights:

📈 Real-time data: Tracks progress
⚠ Spot issues: Spot bottlenecks
💡 Smart choices: Informs decisions

Automation Tools in GitHub Projects

Built-in Automations:

📦 Auto-move tasks based on events
🔃 Keep boards updated automatically

GitHub Actions:

🔧 Automate complex workflows
🔖 Tag and manage tasks efficiently

Implementing Built-in Automations

Built-in Automations

Advanced Automation with GitHub Actions

🚀 Automate workflows: Trigger tasks on code events
📄 YAML-powered: Define actions in YAML files
🛠 Common tasks: Schedule data pipelines or deploy ML models

name: Data Workflow
on: [push]

jobs:
  run-analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run analysis
        run: python analyze_data.py

¹ https://docs.github.com/en/actions

Introduction to Project Insights

📈 Visualize progress: Real-time and historical data charts
⚠ Spot bottlenecks: Identify issues early with insights
🕐 Chart types: Includes both Current and Historical charts

current charts.webp

historical charts.webp

Creating a Chart in Project Insights

Creating a Chart

Let's practice!

Intermediate GitHub Concepts