DevOps Versus MLOps Workflow Comparison
DevOps Workflow:
1. Start DevOps: The process begins.
2. Code Development: Developers write code.
3. Code Review: The written code is reviewed for quality and correctness.
4. Continuous Integration: The code is regularly integrated into a shared repository.
5. Build: The integrated code is built into an executable format.
6. Test: The build is tested to ensure it works correctly.
7. Deploy: The tested build is deployed to a production environment.
8. Monitor: The deployed application is monitored to ensure it runs smoothly.
9. End DevOps: The process ends.
MLOps Workflow:
1. Start MLOps: The process begins.
2. Data Collection: Relevant data is collected for model training.
3. Data Preprocessing: The collected data is cleaned and prepared for use.
4. Model Training: The preprocessed data is used to train the machine learning model.
5. Model Evaluation: The trained model is evaluated for accuracy and performance.
6. Model Deployment: The evaluated model is deployed to a production environment.
7. Model Monitoring: The deployed model is monitored to ensure it performs as expected.
8. Model Retraining: Based on monitoring results, the model is retrained to improve performance.
9. End MLOps: The process ends.
Key Differences:
- DevOps focuses on software development and deployment.
- MLOps includes additional steps for handling data and machine learning models, such as data collection, preprocessing, and model retraining.
The MLOps workflow is more iterative due to the continuous need for model monitoring and retraining, reflecting the dynamic nature of machine learning models.