From the course: MLOps Essentials: Model Development and Integration
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Best practices for solution integration
From the course: MLOps Essentials: Model Development and Integration
Best practices for solution integration
- [Instructor] Let's complete the course by discussing some best practices for solution integration for machine learning. We will extend some of the best practices in other pipelines to the integration pipeline also. Define code promotion policies for various stages in the integration pipeline. Stated policies help everyone understand the requirements and avoid confusion at a later stage. Define acceptance criteria that are measurable either using pass-fail tests or performance and operational metrics. Automate solution integration and testing, as this needs to be done frequently, as the ML and non-ML parts evolve simultaneously. Focus on both the ML and-non ML test cases during integration. And finally, it's important for the ML and non -ML teams to collaborate consistently to make sure that each knows what the other team is working on and can anticipate changes.
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