Fundamentals of MLOps and deployment patterns
To effectively manage MLOps, it’s essential to first familiarize ourselves with its underlying terminology and structure. This includes understanding the roles and responsibilities associated with various operational environments – namely, development (dev), staging, and production (prod). Let’s dissect what these environments signify in a practical MLOps framework.
Within any ML project, there are three pivotal assets:
- Code base: This serves as the project’s blueprint. It contains all the source code related to data preprocessing, model training, evaluation, and deployment.
- Data: This includes the datasets that are used for training, validating, and testing the model. The quality and availability of this data directly influence the model’s efficacy.
- Trained model: This is the culmination of your ML workflow, a model that has been trained, evaluated, and prepared for inference.
