Adopting MLOps for ML workflows
Similar to the DevOps practice, which has been widely adopted for the traditional software development and deployment process, the MLOps practice is intended to streamline the building and deployment processes of ML pipelines and improve the collaborations between data scientists/ML engineers, data engineering, and the operations team. Specifically, an MLOps practice is intended to deliver the following main benefits in an end-to-end ML life cycle:
- Process consistency: The MLOps practice aims to create consistency in the ML model building and deployment process. A consistent process improves the efficiency of the ML workflow and ensures a high degree of certainty in the input and output of the ML workflow.
- Tooling and process reusability: One of the core objectives of the MLOps practice is to create reusable technology tooling and templates for faster adoption and deployment of new ML use cases. These can include common tools such as code...
