Understanding ML deployment patterns
The ultimate goal of any ML project is to get our ML model into production. Depending on what kind of use case we are catering to and how sophisticated our ML engineering team is, there are two broad ML deployment approaches:
- The deploy models approach
- The deploy code approach
Let’s understand these approaches one by one.
The deploy models approach
The model deployment workflow adheres to a structured methodology, beginning in a development environment where code for training the ML model is both crafted and refined. After the model undergoes training and the optimal version is ascertained, it is formally registered within a specialized model registry. This is followed by a battery of integration tests to evaluate its performance and reliability. Upon successfully passing these assessments, the model is first elevated to a staging environment for further validation. Once it meets all requisite criteria, it is then...
