Model metadata and lineage in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to log model metadata using MLflow.

MLOps

import mlflow
mlflow.[1]("learning_rate", 0.01)

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Amodels

Bset_tag

Clog_param

Dstart_run

Attempts:

3 left

💡 Hint

Common Mistakes

Using start_run instead of log_param.

Trying to log parameters with models module.

The log_param function records a parameter like learning rate in MLflow metadata.

2fill in blank

medium

Complete the code to start an MLflow run for tracking.

MLOps

with mlflow.[1]():
    mlflow.log_metric("accuracy", 0.95)

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Aexperiment

Blog_metric

Crun

Dstart_run

Attempts:

3 left

💡 Hint

Common Mistakes

Using log_metric as a context manager.

Using experiment which is not a context manager.

The start_run function starts a new MLflow run context to track metrics and parameters.

3fill in blank

hard

Fix the error in the code to register a model in MLflow Model Registry.

MLOps

mlflow.[1]("runs:/12345/model", "MyModel")

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Aregister_model

Blog_model

Cmodels

Dstart_run

Attempts:

3 left

💡 Hint

Common Mistakes

Using log_model which does not exist.

Using start_run which is unrelated.

The register_model function registers a model in the MLflow Model Registry using the model URI and name.

4fill in blank

hard

Fill both blanks to create a dictionary capturing model metadata and lineage.

MLOps

metadata = {"model_name": [1], "source_run_id": [2]

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A"MyModel"

B"12345"

C"run_67890"

D"ModelV1"

Attempts:

3 left

💡 Hint

Common Mistakes

Using numeric values instead of strings.

Mixing up model name and run ID.

The model_name is a string like "MyModel" and source_run_id is the run identifier string.

5fill in blank

hard

Fill all three blanks to log model metadata with tags and parameters.

MLOps

mlflow.start_run()
mlflow.set_tag("[1]", "v1.0")
mlflow.log_param("[2]", 100)
mlflow.log_metric("[3]", 0.98)

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Aversion

Bnum_trees

Caccuracy

Dstage

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing tags with parameters or metrics.

Using incorrect keys that MLflow does not recognize.

Tags like version describe model version, parameters like num_trees describe model settings, and metrics like accuracy show performance.