AutoML Pipeline Setup

Overview

Automated machine learning pipeline with FLAML and AutoGluon. This guide covers practical implementation for production ML systems.

Why This Matters in MLOps

Setup

bash
Install required tools
pip install flaml 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 AutoMLPipelineSetup:
    """
    AutoML Pipeline Setup implementation.
    
    Handles: automation
    Tool: flaml
    """
    
    def __init__(self, config: dict = None):
        self.config = config or self._default_config()
        self._setup()
    
    def _default_config(self) -> dict:
        return {
            "tool": "flaml",
            "environment": os.getenv("ENVIRONMENT", "development"),
            "log_level": "INFO",
        }
    
    def _setup(self):
        """Initialize flaml connection and resources."""
        logging.basicConfig(level=self.config.get("log_level", "INFO"))
        logger.info(f"Initialized AutoML Pipeline Setup 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"AutoML Pipeline Setup completed in {elapsed:.2f}s")
            
            return {
                "status": "success",
                "result": result,
                "elapsed_seconds": elapsed
            }
        
        except Exception as e:
            logger.error(f"AutoML Pipeline Setup failed: {e}")
            return {
                "status": "failed", 
                "error": str(e)
            }
    
    def _execute(self, **kwargs) -> dict:
        """Core automation logic. Override to customize."""
        return {"completed": True, "tool": "flaml"}
Configuration
config = {
    "tool": "flaml",
    "tracking_uri": os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000"),
    "artifact_root": "./artifacts",
}
Initialize
processor = AutoMLPipelineSetup(config)
result = processor.run()
print(json.dumps(result, indent=2))

FLAML Integration

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

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.automl_pipeline_setup
        env:
          MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
      
      - name: Check model quality
        run: python -m src.validate_model

Best Practices

Resources