MLOPS LIFE CYCLE

1. 1Define the MLOps life cycle stages
2. 2Implement model versioning using tools like Git
3. 3Configure automated testing for model validation
4. 4Deploy models using containerization tools like Docker
5. 5Monitor model performance in production

Data scientists and engineers benefit from understanding the MLOps life cycle to collaborate on model development and deployment

💡 MLOps life cycle helps bridge the gap between model development and deployment

Streamline #MachineLearning model development with #MLOps life cycle 🚀