▎Common MLOps Terms

1. MLOps: A set of practices that automates and standardizes the lifecycle of Machine Learning models, from experimentation and development to deployment and maintenance.

2. Model Training: The process of feeding data to an ML algorithm to learn patterns and make predictions, resulting in a trained model.

3. Feature Store: A centralized repository for storing, serving, and managing features for Machine Learning models, ensuring consistency between training and inference.

4. Data Versioning: The practice of tracking changes to datasets over time, ensuring reproducibility and allowing rollbacks to previous versions.

5. Model Versioning: Managing different iterations of a Machine Learning model, tracking changes, performance, and metadata.

6. Experiment Tracking: Recording all details of an ML experiment (code, hyperparameters, data, metrics) to compare results and ensure reproducibility.

7. Model Registry: A centralized hub to manage the lifecycle of ML models, including versioning, metadata, and status (e.g., "staging," "production").

8. Model Deployment: The process of making a trained ML model available for predictions in a production environment, often via an API endpoint.

9. Inference: The process of using a deployed ML model to make predictions on new, unseen data.

10. Model Monitoring: Continuously tracking the performance, health, and behavior of deployed ML models to detect issues like data drift or performance degradation.

11. Continuous Training (CT): The practice of automatically retraining and updating ML models in production based on new data or performance metrics.

12. Reproducibility: The ability to achieve the same results (model, predictions) from an ML experiment given the same data, code, and environment.

13. Data Drift: A change in the distribution of input data to an ML model, which can cause performance degradation.

14. Concept Drift: A change in the underlying relationship between the input data and the target variable, leading to model inaccuracy over time.

15. Bias Detection: Identifying and mitigating unfair or discriminatory patterns in ML models or their data, ensuring ethical AI outcomes.

16. ML Pipeline: An automated workflow for running an ML task, encompassing data ingestion, feature engineering, model training, evaluation, and deployment steps.

17. Orchestration: Managing and coordinating the automated tasks within an ML pipeline to ensure they run in the correct sequence and handle dependencies.

18. Explainable AI (XAI): Tools and techniques that make the decisions and predictions of ML models understandable to humans.

19. Serving Infrastructure: The systems and platforms used to host and serve ML models in production, optimized for low-latency inference (e.g., REST APIs, specialized model servers).

20. ML Metadata Management: Storing and organizing information about ML artifacts (datasets, models, features, experiments) to provide lineage and ensure governance.