What are the key components of an MLOps architecture?

An effective MLOps architecture typically includes several key components: 1. **Data Management**: Systems for data ingestion, storage, and processing to ensure high-quality, accessible data for training and inference. 2. **Model Development**: Tools and frameworks for training models, which may include Jupyter notebooks, version control (like Git), and libraries such as TensorFlow or PyTorch. 3. **Model Registry**: A centralized repository for storing models, allowing for version control and easy retrieval. 4. **Continuous Integration/Continuous Deployment (CI/CD)**: Pipelines for automating the deployment of models, including testing and integration phases to enhance collaboration and speed. 5. **Monitoring and Logging**: Systems to track model performance in real-time, detect drift, and log metadata for reproducibility and accountability. 6. **Infrastructure Management**: Tools to provision and manage the underlying hardware or cloud resources needed for model training and serving.