MLOps Toolkit 🛠️

Enterprise-grade ML infrastructure and deployment tools. Comprehensive suite of tools and implementations for managing ML lifecycle, experiments, and deployments.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Components
  • Integration
  • Benchmarks
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Automated ML pipelines
• Experiment tracking and versioning
• Model registry and deployment
• A/B testing framework
• Monitoring and alerting
• Feature store implementation

📁 Project Structure

graph TD
    A[mlops-toolkit] --> B[pipelines]
    A --> C[monitoring]
    A --> D[registry]
    A --> E[deployment]
    B --> F[training]
    B --> G[evaluation]
    C --> H[metrics]
    C --> I[alerts]
    D --> J[models]
    D --> K[artifacts]
    E --> L[kubernetes]
    E --> M[serving]

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Click to expand full directory structure

mlops-toolkit/
├── pipelines/         # ML pipelines
│   ├── training/     # Training pipelines
│   └── evaluation/   # Evaluation pipelines
├── monitoring/       # Monitoring suite
│   ├── metrics/     # Metrics collection
│   └── alerts/      # Alerting system
├── registry/         # Model registry
├── deployment/       # Deployment tools
├── tests/           # Unit tests
└── README.md        # Documentation

🔧 Prerequisites

• Python 3.8+
• MLflow 2.9+
• DVC 3.30+
• Kubernetes 1.24+
• PostgreSQL 13+

📦 Installation

# Clone repository
git clone https://github.com/BjornMelin/mlops-toolkit.git
cd mlops-toolkit

# Create environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Initialize infrastructure
make init-infrastructure

🚀 Quick Start

from mlops_toolkit import pipeline, monitoring

# Create training pipeline
pipeline = pipeline.MLPipeline(
    name="training-pipeline",
    steps=[
        pipeline.DataPrep(),
        pipeline.Training(),
        pipeline.Evaluation()
    ]
)

# Configure monitoring
monitoring = monitoring.ModelMonitoring(
    metrics=["accuracy", "latency"],
    alerts_config={
        "accuracy_threshold": 0.95,
        "latency_p95_ms": 100
    }
)

# Run pipeline with monitoring
pipeline.run(monitoring=monitoring)

📚 Documentation

Components

Component	Purpose	Integration Points	Scalability
Model Registry	Version Control	Git, DVC	High
Feature Store	Feature Management	PostgreSQL, Redis	Very High
Monitoring	Performance Tracking	Prometheus, Grafana	High
Pipeline Orchestration	Workflow Management	Airflow, Kubernetes	High

Integration

• CI/CD pipeline integration
• Kubernetes deployment
• Cloud provider support
• Monitoring stack setup

Benchmarks

System performance metrics:

Operation	Scale	Latency	Throughput
Model Registration	100 models/day	2s	50 ops/sec
Feature Serving	10TB dataset	20ms	10k req/sec
Pipeline Execution	50 concurrent	5min	20 jobs/min

🤝 Contributing

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024mlopstoolkit,
  author = {Melin, Bjorn},
  title = {MLOps Toolkit: Enterprise ML Infrastructure Tools},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/mlops-toolkit}
}

📄 License

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

🙏 Acknowledgments

• MLflow community
• DVC team
• Kubernetes contributors
• Open source MLOps community

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Made with 🛠️ and ❤️ by Bjorn Melin