In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?
A.
The launch of a new cost-efficient SQL endpoint
B.
CI/CD pipelines are not needed for machine learning pipelines
C.
The arrival of a new feature table in the Feature Store
D.
The launch of a new cost-efficient job cluster
E.
The arrival of a new model version in the MLflow Model Registry
In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, the arrival of a new model version in the MLflow Model Registry is a common event that triggers the execution of automated testing. This is because the MLflow Model Registry is a centralized repository for managing the full lifecycle of MLflow models. It provides model lineage, model versioning, stage transitions, and annotations. When a new model version is registered, it can be automatically tested for quality, performance, and compliance using various tools and frameworks, such as MLflow Projects, MLflow Models, MLflow Tracking, and MLflow Model Serving. The automated testing can help ensure that the new model version meets the desired criteria and standards before it is deployed to production or other stages. The automated testing can also help detect and prevent any errors, bugs, or drifts that may affect the model functionality or accuracy. References:
MLflow Model Registry Documentation, p. 1-2
MLOps: Continuous delivery and automation pipelines in machine learning, Continuous integration and delivery (CI/CD) for ML, p. 5-6
Machine Learning Engineering with MLflow, Testing and Validating ML Models, p. 1-2
CI/CD for Machine Learning: What it is & Benefits in 2023 - AIMultiple, CI/CD for Machine Learning, p. 1-2
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