Continuous Training Pipelines

Overview

Automated model retraining triggered by data or performance changes. This guide covers practical implementation for production ML systems.

Why This Matters in MLOps

Setup

bash
Install required tools
pip install airflow mlflow pandas numpy scikit-learn
Or with Docker
docker pull python:3.11-slim

Core Implementation

python
import os
import json
import logging
from datetime import datetime
from pathlib import Path
logger = logging.getLogger(__name__)
class ContinuousTrainingPipelines:
    """
    Continuous Training Pipelines implementation.
    
    Handles: automation
    Tool: airflow
    """
    
    def __init__(self, config: dict = None):
        self.config = config or self._default_config()
        self._setup()
    
    def _default_config(self) -> dict:
        return {
            "tool": "airflow",
            "environment": os.getenv("ENVIRONMENT", "development"),
            "log_level": "INFO",
        }
    
    def _setup(self):
        """Initialize airflow connection and resources."""
        logging.basicConfig(level=self.config.get("log_level", "INFO"))
        logger.info(f"Initialized Continuous Training Pipelines with config: {self.config}")
    
    def run(self, **kwargs) -> dict:
        """Execute automation."""
        start = datetime.utcnow()
        
        try:
            result = self._execute(**kwargs)
            
            elapsed = (datetime.utcnow() - start).total_seconds()
            logger.info(f"Continuous Training Pipelines completed in {elapsed:.2f}s")
            
            return {
                "status": "success",
                "result": result,
                "elapsed_seconds": elapsed
            }
        
        except Exception as e:
            logger.error(f"Continuous Training Pipelines failed: {e}")
            return {
                "status": "failed", 
                "error": str(e)
            }
    
    def _execute(self, **kwargs) -> dict:
        """Core automation logic. Override to customize."""
        return {"completed": True, "tool": "airflow"}
Configuration
config = {
    "tool": "airflow",
    "tracking_uri": os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000"),
    "artifact_root": "./artifacts",
}
Initialize
processor = ContinuousTrainingPipelines(config)
result = processor.run()
print(json.dumps(result, indent=2))

AIRFLOW Integration

python
Specific airflow integration for automation
import subprocess
def setup_airflow():
    """Configure airflow for automation."""
    
    # Initialize project
    print(f"Setting up airflow for automation...")
    
    # Example configuration
    config = {
        "project": "my-ml-project",
        "tool": "airflow",
        "specialty": "automation",
        "version": "1.0.0"
    }
    
    # Save configuration
    Path(".airflow").mkdir(exist_ok=True)
    with open(f".airflow/config.json", "w") as f:
        json.dump(config, f, indent=2)
    
    print(f"airflow configured for automation")
    return config
config = setup_airflow()

Monitoring and Alerting

python
from dataclasses import dataclass
import time
class MLOpsMonitor:
    """Monitor automation metrics."""
    
    def __init__(self):
        self.metrics: list[MetricSnapshot] = []
        self.thresholds = {
            "error_rate": 0.05,
            "latency_p99_ms": 1000,
            "data_drift_score": 0.3
        }
    
    def record(self, metric: str, value: float, labels: dict = None):
        snapshot = MetricSnapshot(
            timestamp=time.time(),
            metric_name=metric,
            value=value,
            labels=labels or {}
        )
        self.metrics.append(snapshot)
        self._check_threshold(metric, value)
    
    def _check_threshold(self, metric: str, value: float):
        threshold = self.thresholds.get(metric)
        if threshold and value > threshold:
            logger.warning(f"ALERT: {metric}={value:.3f} exceeds threshold {threshold}")
monitor = MLOpsMonitor()

CI/CD Integration

yaml
.github/workflows/ml-pipeline.yml
name: ML Pipeline
on:
  push:
    paths: ['src/', 'data/']
jobs:
  train-and-evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      
      - name: Install dependencies
        run: pip install -r requirements.txt
      
      - name: Run automation
        run: python -m src.continuous_training_pipelines
        env:
          MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
      
      - name: Check model quality
        run: python -m src.validate_model

Best Practices

Resources