Model Versioning
In MLOps, model versioning is the process of tracking changes to a machine learning model over time. Implement a function to create a new version of a model using the NumPy library and MLflow.
Example:
Suppose we have a trained linear regression model represented as a NumPy array. We want to create a new version of the model with updated weights and biases.
Constraints:
Test Cases
Test Case 1
Input:
[[1.0, 2.0], [3.0, 4.0]]Expected:
nullTest Case 2
Input:
[[5.0, 6.0], [7.0, 8.0]]Expected:
null+ 3 hidden test cases