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MLOps Pipeline Guide

Complete guide to setting up and managing MLOps pipelines in Local AI Cyber Lab, covering automation, monitoring, and best practices for machine learning operations.

Overview

The MLOps pipeline in Local AI Cyber Lab provides an end-to-end workflow for machine learning operations, from development to deployment and monitoring.

Pipeline Components

Development Environment

  • Jupyter Lab for experimentation
  • VS Code integration
  • Git version control
  • Development containers

Training Infrastructure

  • MLflow experiment tracking
  • Distributed training support
  • GPU resource management
  • Dataset versioning

Model Registry

  • Version control
  • Model metadata
  • Artifact storage
  • Deployment history

Deployment Pipeline

  • Continuous Integration
  • Automated testing
  • Deployment automation
  • Rollback mechanisms

Setting Up MLOps Pipeline

1. Initialize Development Environment

# Start Jupyter Lab
docker-compose up jupyter

# Configure Git integration
git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

2. Configure MLflow

# mlflow-config.yaml
mlflow:
  tracking_uri: http://mlflow:5000
  experiment_name: my-experiment
  artifact_location: s3://mlflow-artifacts
  registry_uri: http://mlflow:5000

3. Setup Model Registry

import mlflow
from mlflow.tracking import MlflowClient

# Initialize client
client = MlflowClient()

# Create new model version
model_version = client.create_model_version(
    name="my-model",
    source="s3://model-artifacts/model.pkl",
    run_id="run_123"
)

Pipeline Workflows

Training Pipeline

import mlflow

def training_pipeline(data, config):
    with mlflow.start_run():
        # Log parameters
        mlflow.log_params(config)

        # Train model
        model = train_model(data, config)

        # Log metrics
        mlflow.log_metrics({
            "accuracy": model.accuracy,
            "loss": model.loss
        })

        # Save model
        mlflow.pytorch.log_model(model, "model")

Deployment Pipeline

def deployment_pipeline(model_name, version):
    # Load model from registry
    model = mlflow.pyfunc.load_model(
        f"models:/{model_name}/{version}"
    )

    # Deploy model
    deploy_model(model, {
        "endpoint": "/predict",
        "replicas": 3
    })

Monitoring & Logging

MLflow Tracking

# Track experiments
mlflow.set_experiment("my-experiment")

with mlflow.start_run():
    # Log parameters
    mlflow.log_param("learning_rate", 0.01)

    # Log metrics
    mlflow.log_metric("accuracy", 0.95)

    # Log artifacts
    mlflow.log_artifact("model.pkl")

Prometheus Metrics

# prometheus-config.yaml
scrape_configs:
  - job_name: 'mlflow'
    static_configs:
      - targets: ['mlflow:5000']

  - job_name: 'model-serving'
    static_configs:
      - targets: ['model-api:8080']

CI/CD Integration

GitHub Actions Workflow

name: MLOps Pipeline

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  train:
    runs-on: self-hosted
    steps:
      - uses: actions/checkout@v2
      - name: Train Model
        run: python train.py

  deploy:
    needs: train
    runs-on: self-hosted
    steps:
      - name: Deploy Model
        run: python deploy.py

Best Practices

Version Control

  1. Use Git for code versioning
  2. Version control data and models
  3. Maintain documentation
  4. Track experiments

Testing

  1. Unit tests for components
  2. Integration tests
  3. Model validation
  4. Performance testing

Monitoring

  1. Track model performance
  2. Monitor resource usage
  3. Alert on anomalies
  4. Log all operations

Troubleshooting

Common Issues

  1. Pipeline failures
  2. Resource constraints
  3. Version conflicts
  4. Integration issues

Solutions

  1. Check logs
  2. Verify configurations
  3. Test integrations
  4. Monitor resources

Support

For MLOps assistance: - 📧 Email: support@cyber-ai-agents.com - 📚 Documentation - 💬 Community Forum