Searching Models

As the MLFlow Model Registry grows and you collaborate with other users, you need to be able to search the Model Registry for models that fit certain criteria.

In this exercise, you will create two filter strings to search the MLflow Model Registry for Models. The first filter string searches for all "Insurance" Models and the second filter string searches Non-"Insurance" Models.

Models have already been created by other collaborators using the same MLflow Model Registry and an instance of the MLflow Client module will already be set as client.

client = mlflow.MlflowClient()

The MLflow Client module will be imported.

This exercise is part of the course

Introduction to MLflow

Exercise instructions

Create a filter string that finds all models with a name prefix 'Insurance'.
Search the Model Registry using the insurance_filter_string.
Create a filter that finds models that are not equal to the name 'Insurance'.
Search the Registry with not_insurance_filter_string filter string variable.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Insurance filter string
insurance_filter_string = "____ ____ '____'"

# Search for Insurance models
print(____.____(____=____))

# Not Insurance filter string
not_insurance_filter_string = "____ ____ '____'"

# Search for non Insurance models
print(____.____(____=____))

Edit and Run Code

This exercise is part of the course

Introduction to MLflow

AdvancedSkill Level

4.7+

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In this Chapter, you will be introduced to MLflow and how it aims to assist with some difficulties of the Machine Learning lifecycle. You will be introduced to the four main concepts that make up MLflow with a main focus on MLflow Tracking. You will learn to create experiments and runs as well as how to track metrics, parameters, and artifacts. Finally, you will search MLflow programmatically to find experiment runs that fit certain criteria.

Exercise 1: What is MLflow?Exercise 2: Components of MLflow Exercise 3: MLflow experiments Exercise 4: MLflow Tracking Exercise 5: Starting a run Exercise 6: Logging a run Exercise 7: How to retrieve active run data?Exercise 8: Querying runs Exercise 9: Search runs query options Exercise 10: Searching runs

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Exercise 1: Introduction to MLflow Models Exercise 2: Package a machine learning model Exercise 3: Storage Format Exercise 4: What's in an MLmodel file?Exercise 5: Model API Exercise 6: Saving and loading a model Exercise 7: Logging and loading a model Exercise 8: Custom models Exercise 9: Creating a custom Python Class Exercise 10: Custom scikit-learn model Exercise 11: Scikit-learn flavor and evaluation Exercise 12: Model serving Exercise 13: Serving a model Exercise 14: Score from a served model

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Exercise 1: Introduction to MLflow Model Registry Exercise 2: Create a Model Exercise 3: Searching Models

Current Exercise

Exercise 4: Registering Models Exercise 5: Registering existing models Exercise 6: Registering new models Exercise 7: Model stages Exercise 8: Model stages in Model Registry Exercise 9: Transitioning model stages Exercise 10: Model deployment Exercise 11: Serving models from the Model Registry Exercise 12: Loading models from the Model Registry

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Exercise 1: Introduction to MLflow Projects Exercise 2: MLproject file layout Exercise 3: Creating an MLproject Exercise 4: Running MLflow Projects Exercise 5: MLflow run command Exercise 6: MLflow projects module Exercise 7: Specifying parameters Exercise 8: Adding parameters to MLproject Exercise 9: Adding parameters to project run Exercise 10: Workflows Exercise 11: Creating an MLproject for the ML Lifecycle: Model Engineering Exercise 12: Creating an MLproject for the ML Lifecycle: Model Evaluation Exercise 13: Creating a multi-step workflow: Model Engineering Exercise 14: Creating a multi-step workflow: Model Evaluation Exercise 15: Wrap-up