What does MLOps Pipeline do? (Quick steps)

Dive into the world of MLOps: A step-by-step guide to building efficient machine learning pipelines.

1: Data Collection - The pipeline begins by collecting and ingesting data from various sources (e.g., databases, APIs, or streaming data). 2: Data Preprocessing - Raw data is cleaned, transformed, and normalized to ensure quality and consistency for model training. 3: Feature Engineering - Relevant features are extracted or created from the processed data to improve model accuracy. 4: Model Training - Machine learning models are trained using the prepared data, employing algorithms and tuning hyperparameters to optimize performance. 5: Model Validation - The pipeline validates the trained models using a separate validation dataset to assess accuracy, precision, recall, and other metrics. 6: Model Versioning - Different versions of the model are tracked and stored in a model registry, ensuring traceability and reproducibility. 7: Model Testing - The pipeline tests the model in a staging environment to check for issues related to scalability, latency, and real-time performance. 8: Model Deployment - The validated model is deployed into a production environment, ready to serve predictions to end-users or other systems. 9: Continuous Integration/Continuous Deployment (CI/CD) - Automated CI/CD processes ensure that updates to data, code, or models are seamlessly integrated and deployed with minimal downtime. 10: Monitoring and Logging - The pipeline monitors the model's performance in production, tracking metrics like drift, accuracy, and resource usage. Logs are generated for further analysis. 11: Model Retraining - When performance metrics indicate model degradation, the pipeline automatically triggers retraining using the latest data. 12: Governance and Compliance - The pipeline enforces policies for data security, model fairness, and regulatory compliance throughout the lifecycle.