mlops-forge 1.0.0

A complete production-ready MLOps framework with built-in distributed training, monitoring, and CI/CD.

MLOps-Forge

A complete production-ready MLOps framework with built-in distributed training, monitoring, and CI/CD. Deploy ML models to production with confidence using our battle-tested infrastructure. This project implements an end-to-end ML pipeline that follows industry best practices for developing, deploying, and maintaining ML models in production environments at scale.

📋 Table of Contents

• Features
• Architecture
  • Component Details
  • System Flow
• Getting Started
  • Prerequisites
  • Installation
  • Configuration
• Usage
  • Data Pipeline
  • Model Training
  • Model Deployment
  • Monitoring
• CI/CD Pipeline
• Development
  • Project Structure
  • Contributing
• Advanced Usage
  • Distributed Training
  • A/B Testing
  • Drift Detection
• Security
• License

🚀 Features

• Automated Data Pipeline: Robust data validation, cleaning, and feature engineering
• Experiment Tracking: Comprehensive version control for models, datasets, and hyperparameters with MLflow
• Distributed Training: GPU-accelerated training across multiple nodes for large models
• Model Registry: Centralized model storage and versioning with lifecycle management
• Continuous Integration/Deployment: Automated testing, validation, and deployment pipelines
• Model Serving API: Fast and scalable REST API with input validation and automatic documentation
• Model Monitoring: Performance tracking, drift detection, and automated retraining triggers
• A/B Testing: Framework for model experimentation and controlled rollouts
• Infrastructure as Code: Docker containers and Kubernetes configurations for reliable deployments

🏗️ Architecture

This system follows a modular microservice architecture with the following components:

graph TD
    %% Main title and styles
    classDef pipeline fill:#f0f6ff,stroke:#3273dc,color:#3273dc,stroke-width:2px
    classDef component fill:#ffffff,stroke:#209cee,color:#209cee,stroke-width:1.5px
    classDef note fill:#fffaeb,stroke:#ffdd57,color:#946c00,stroke-width:1px,stroke-dasharray:5 5
    classDef infra fill:#e3fcf7,stroke:#00d1b2,color:#00d1b2,stroke-width:1.5px,stroke-dasharray:5 5
    
    %% Infrastructure
    subgraph K8S["Kubernetes Cluster"]
        %% Data Pipeline
        subgraph DP["Data Pipeline"]
            DI[Data Ingestion]:::component
            DV[Data Validation]:::component
            FE[Feature Engineering]:::component
            FSN[Feature Store Integration]:::note
            
            DI --> DV
            DV --> FE
        end
        
        %% Model Training
        subgraph MT["Model Training"]
            ET[Experiment Tracking - MLflow]:::component
            DT[Distributed Training]:::component
            ME[Model Evaluation]:::component
            ABN[A/B Testing Framework]:::note
            
            ET --> DT
            DT --> ME
        end
        
        %% Model Registry
        subgraph MR["Model Registry"]
            MV[Model Versioning]:::component
            MS[Metadata Storage]:::component
            MCI[CI/CD Integration]:::note
            
            MV --> MS
        end
        
        %% API Layer
        subgraph API["API Layer"]
            FA[FastAPI Application]:::component
            PE[Prediction Endpoints]:::component
            HM[Health & Metadata APIs]:::component
            HPA[Horizontal Pod Autoscaling]:::note
            
            FA --> PE
            FA --> HM
        end
        
        %% Monitoring
        subgraph MON["Monitoring"]
            PM[Prometheus Metrics]:::component
            GD[Grafana Dashboards]:::component
            DD[Feature-level Drift Detection]:::component
            RT[Automated Retraining Triggers]:::component
            AM[Alert Manager Integration]:::note
            
            MPT[Model Performance Tracking]:::component
            DQM[Data Quality Monitoring]:::component
            ABT[A/B Testing Analytics]:::component
            LA[Log Aggregation]:::component
            DT2[Distributed Tracing]:::note
            
            PM --> GD
            PM --> DD
            DD --> RT
            MPT --> DQM
            DQM --> ABT
            ABT --> LA
        end
        
        %% Component relationships
        DP -->|Training Data| MT
        DP -->|Metadata| MR
        MT -->|Model Artifacts| MR
        MR -->|Latest Model| API
        API -->|Metrics| MON
        MT -->|Performance Metrics| MON
    end
    
    %% CI/CD Pipeline
    CICD[CI/CD Pipeline: GitHub Actions]:::infra
    CICD -->|Deploy| K8S
    
    %% Apply classes
    class DP,MT,MR,API,MON pipeline

Component Details

1. Data Pipeline

   • Data Ingestion: Connectors for various data sources (databases, object storage, streaming)
   • Data Validation: Schema validation, data quality checks, and anomaly detection
   • Feature Engineering: Feature transformation, normalization, and feature store integration

2. Model Training

   • Experiment Tracking: MLflow integration for tracking parameters, metrics, and artifacts
   • Distributed Training: PyTorch distributed training for efficient model training
   • Model Evaluation: Comprehensive metrics calculation and validation

3. Model Registry

   • Model Versioning: Storage and versioning of models with metadata
   • Artifact Management: Efficient storage of model artifacts and associated files
   • Deployment Management: Tracking of model deployment status

4. API Layer

   • FastAPI Application: High-performance API with automatic OpenAPI documentation
   • Prediction Endpoints: RESTful endpoints for model inference
   • Health & Metadata: Endpoints for system health checks and model metadata

5. Monitoring System

   • Metrics Collection: Prometheus integration for metrics collection
   • Drift Detection: Statistical methods to detect data and concept drift
   • Performance Tracking: Continuous monitoring of model performance metrics
   • Automated Retraining: Triggers for retraining based on drift detection

System Flow

1. Development Workflow:

   flowchart LR
       DS[Data Scientist] --> |Develops Model| DEV[Development Environment]
       DEV --> |Commits Code| GIT[Git Repository]
       GIT --> |Triggers| CI[CI/CD Pipeline]
       CI --> |Runs Tests| TEST[Test Suite]
       TEST --> |Validates Model| VAL[Model Validation]
       VAL --> |Performance Testing| PERF[Performance Tests]
       PERF --> |Builds| BUILD[Docker Image]
       BUILD --> |Deploys| DEPLOY[Kubernetes Cluster]
   

2. Production Data Flow:

   flowchart LR
       DATA[Data Sources] --> |Ingestion| PIPE[Data Pipeline]
       PIPE --> |Validated Data| TRAIN[Training Pipeline]
       TRAIN --> |Trained Model| REG[Model Registry]
       REG --> |Latest Model| API[API Service]
       API --> |Predictions| USERS[End Users]
       API --> |Metrics| MON[Monitoring]
       MON --> |Drift Detected| RETRAIN[Retraining Trigger]
       RETRAIN --> TRAIN
   

🚦 Getting Started

Prerequisites

• Python 3.9+ with pip
• Docker and Docker Compose
• Kubernetes cluster (local or cloud-based)
• AWS account (for cloud deployment)

Installation

# Clone the repository
git clone https://github.com/TaimoorKhan10/MLOps-Forge.git
cd MLOps-Forge

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

Configuration

1. Environment Variables: Create a .env file based on the provided .env.example:

   # MLflow Configuration
   MLFLOW_TRACKING_URI=http://mlflow:5000
   MLFLOW_S3_ENDPOINT_URL=http://minio:9000
   
   # AWS Configuration for Deployment
   AWS_ACCESS_KEY_ID=your-access-key
   AWS_SECRET_ACCESS_KEY=your-secret-key
   AWS_REGION=us-west-2
   
   # Kubernetes Configuration
   K8S_NAMESPACE=mlops-production
   

2. Infrastructure Setup:

   # For local development with Docker Compose
   docker-compose up -d
   
   # For Kubernetes deployment
   kubectl apply -f infrastructure/kubernetes/
   

🧰 Usage

Data Pipeline

from mlops_production_system.pipeline import DataPipeline

# Initialize the pipeline
pipeline = DataPipeline(config_path="config/pipeline_config.yaml")

# Run the pipeline
processed_data = pipeline.run(input_data_path="data/raw/training_data.csv")

Model Training

from mlops_production_system.models import ModelTrainer
from mlops_production_system.training import distributed_trainer

# For single-node training
trainer = ModelTrainer(model_config="config/model_config.yaml")
model = trainer.train(X_train, y_train)
metrics = trainer.evaluate(X_test, y_test)

# For distributed training
distributed_trainer.run(
    model_class="mlops_production_system.models.CustomModel",
    data_path="data/processed/training_data.parquet",
    num_nodes=4
)

Model Deployment

# Deploy model using CLI
mlops deploy --model-name="my-model" --model-version=1 --environment=production

# Or using the Python API
from mlops_production_system.deployment import ModelDeployer

deployer = ModelDeployer()
deployer.deploy(model_name="my-model", model_version=1, environment="production")

Monitoring

from mlops_production_system.monitoring import DriftDetector, PerformanceMonitor

# Monitor for drift
drift_detector = DriftDetector(reference_data="data/reference.parquet")
drift_results = drift_detector.detect(new_data="data/production_data.parquet")

# Monitor model performance
performance_monitor = PerformanceMonitor(model_name="my-model", model_version=1)
performance_metrics = performance_monitor.get_metrics(timeframe="last_24h")

🔄 CI/CD Pipeline

The system uses GitHub Actions for CI/CD pipeline, configured in .github/workflows/main.yml. The pipeline includes:

1. Code Quality:

   • Linting with flake8
   • Type checking with mypy
   • Security scanning with bandit

2. Testing:

   • Unit tests with pytest
   • Integration tests
   • Code coverage reporting

3. Model Validation:

   • Performance benchmarking
   • Model quality checks
   • Validation against baseline metrics

4. Deployment:

   • Docker image building
   • Image pushing to container registry
   • Kubernetes deployment updates

All secrets and credentials are stored securely in GitHub Secrets and only accessed during workflow execution.

👨‍💻 Development

Project Structure

MLOps-Production-System/
├── .github/                  # GitHub Actions workflows
├── config/                   # Configuration files
├── data/                     # Data directories (gitignored)
├── docs/                     # Documentation
├── infrastructure/           # Infrastructure as code
│   ├── docker/               # Docker configurations
│   ├── kubernetes/           # Kubernetes manifests
│   └── terraform/            # Terraform for cloud resources
├── notebooks/                # Jupyter notebooks
├── scripts/                  # Utility scripts
├── src/                      # Source code
│   └── mlops_production_system/
│       ├── api/              # FastAPI application
│       ├── models/           # ML models
│       ├── pipeline/         # Data pipeline
│       ├── training/         # Training code
│       ├── monitoring/       # Monitoring tools
│       └── utils/            # Utilities
├── tests/                    # Test suite
├── .env.example              # Example environment variables
├── Dockerfile                # Main Dockerfile
├── pyproject.toml            # Project metadata
└── README.md                 # This file

Contributing

We follow the GitFlow branching model:

1. Create a feature branch from develop: git checkout -b feature/your-feature
2. Make your changes and commit: git commit -m "Add feature"
3. Push your branch: git push origin feature/your-feature
4. Open a Pull Request against the develop branch

All PRs must pass CI checks and code review before being merged.

🔬 Advanced Usage

Distributed Training

The system supports distributed training using PyTorch's DistributedDataParallel for efficient multi-node training:

# Example Kubernetes configuration in infrastructure/kubernetes/distributed-training.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: distributed-training
spec:
  parallelism: 4
  template:
    spec:
      containers:
      - name: trainer
        image: your-registry/mlops-trainer:latest
        resources:
          limits:
            nvidia.com/gpu: 1
        env:
        - name: WORLD_SIZE
          value: "4"

A/B Testing

The A/B testing framework allows comparing multiple models in production:

from mlops_production_system.monitoring import ABTestingFramework

# Set up A/B test between two models
ab_test = ABTestingFramework()
ab_test.create_experiment(
    name="pricing_model_comparison",
    models=["pricing_model_v1", "pricing_model_v2"],
    traffic_split=[0.5, 0.5],
    evaluation_metric="conversion_rate"
)

# Get results
results = ab_test.get_results(experiment_name="pricing_model_comparison")

Drift Detection

Detect data drift to trigger model retraining:

from mlops_production_system.monitoring import DriftDetector

# Initialize with reference data distribution
detector = DriftDetector(
    reference_data="s3://bucket/reference_data.parquet",
    features=["feature1", "feature2", "feature3"],
    drift_method="wasserstein",
    threshold=0.1
)

# Check for drift in new data
drift_detected, drift_metrics = detector.detect(
    current_data="s3://bucket/production_data.parquet"
)

if drift_detected:
    # Trigger retraining
    from mlops_production_system.training import trigger_retraining
    trigger_retraining(model_name="my-model")

🔒 Security

This project follows security best practices:

• Secrets management via environment variables and Kubernetes secrets
• Regular dependency scanning for vulnerabilities
• Least privilege principle for all service accounts
• Network policies to restrict pod-to-pod communication
• Encryption of data at rest and in transit

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

MLOps-Forge was created to demonstrate end-to-end machine learning operations and follows industry best practices for deploying ML models in production environments. Star us on GitHub if you find this project useful!

🔧 Technologies

• ML Framework: scikit-learn, PyTorch
• Feature Store: feast
• Experiment Tracking: MLflow
• API: FastAPI
• Containerization: Docker
• Orchestration: Kubernetes
• CI/CD: GitHub Actions
• Infrastructure as Code: Terraform
• Monitoring: Prometheus, Grafana

🛠️ Installation

Prerequisites

• Python 3.9+
• Docker and Docker Compose
• Kubernetes (optional for local development)

Setup

1. Clone the repository

   git clone https://github.com/TaimoorKhan10/MLOps-Production-System.git
   cd MLOps-Production-System
   

2. Create a virtual environment and install dependencies

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your configuration
   

4. Start the development environment

   docker-compose up -d
   

📊 Demo

Access the demo application at http://localhost:8000 after starting the containers.

The demo includes:

• Model training dashboard
• Real-time inference API
• Performance monitoring

📚 Documentation

Comprehensive documentation is available in the /docs directory:

• Data Pipeline
• Model Training
• API Reference
• Deployment Guide
• Monitoring

🧪 Testing

Run the test suite:

pytest

🚢 Deployment

Local Deployment

docker-compose up -d

Cloud Deployment (AWS)

cd infrastructure/terraform
terraform init
terraform apply

📈 Monitoring

Access the monitoring dashboard at http://localhost:3000 after deployment.

🤝 Contributing

Contributions are welcome! Please check out our contribution guidelines.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.