Logging artifacts and models in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

What is the output of this MLflow artifact logging command?

Consider the following Python code snippet using MLflow to log an artifact file. What will be the output printed after running this code?

MLOps

import mlflow
with open('model_info.txt', 'w') as f:
    f.write('Model version 1.0')
with mlflow.start_run() as run:
    mlflow.log_artifact('model_info.txt')
    print(run.info.artifact_uri)

A file:///path/to/mlruns/0/<run_id>/artifacts

B s3://mlflow-bucket/artifacts/<run_id>

C http://localhost:5000/api/2.0/mlflow/artifacts/<run_id>

D Error: artifact_uri attribute not found

💡 Hint

The artifact_uri shows the local path where artifacts are stored during the run.

MLflow stores artifacts locally by default under the mlruns directory. The artifact_uri attribute gives the local file path for the current run's artifacts.

🧠 Conceptual

intermediate

Which MLflow command logs a model for later deployment?

You want to save a trained scikit-learn model in MLflow so it can be deployed later. Which command correctly logs the model?

A mlflow.log_metric('model', model)

B mlflow.sklearn.log_model(model, 'model')

C mlflow.log_param('model', model)

D mlflow.log_artifact(model, 'model')

💡 Hint

Logging a model uses a specific MLflow flavor function.

mlflow.sklearn.log_model saves the model in a format MLflow can use for deployment. log_artifact is for files, log_param and log_metric are for parameters and metrics.

❓ Troubleshoot

advanced

Why does MLflow fail to log artifacts in this code?

You run this code but no artifacts appear in the MLflow UI. What is the most likely cause?

MLOps

import mlflow
with mlflow.start_run():
    mlflow.log_artifact('output.txt')
# output.txt is created after the run block

A The artifact file does not exist when log_artifact is called

B output.txt must be a directory, not a file

C log_artifact only works outside the run context

D MLflow requires explicit run ID to log artifacts

💡 Hint

Check when the file is created relative to when it is logged.

MLflow cannot log a file that does not exist at the time log_artifact is called. The file must be created before logging.

🔀 Workflow

advanced

Order the steps to log a model and its metrics in MLflow

Arrange these steps in the correct order to log a model and its evaluation metrics using MLflow.

A 1,3,2,4,5

B 1,2,3,4,5

C 2,1,4,3,5

D 2,1,3,4,5

💡 Hint

You must start the run before logging anything.

The correct workflow is to start the run, train the model, log the model, log metrics, then end the run.

✅ Best Practice

expert

What is the best practice for versioning models in MLflow?

You want to keep track of multiple versions of your models in MLflow for easy rollback and comparison. Which approach is best?

A Store models in separate folders outside MLflow

B Manually rename model files with version numbers before logging

C Use MLflow Model Registry to register and version models

D Log models without versioning and rely on timestamps

💡 Hint

MLflow provides a built-in system for model versioning.

MLflow Model Registry is designed to manage model versions, stages, and metadata centrally. Manual renaming or external storage is error-prone.

Practice

1. What is the main purpose of logging artifacts and models in MLOps?

easy

A. To speed up model training

B. To delete old models automatically

C. To create new datasets from artifacts

D. To save files and models for tracking and reuse

Solution

1. Step 1: Understand the role of logging in MLOps

   Logging artifacts and models helps keep track of work and reuse it later.

2. Step 2: Identify the correct purpose

   Saving files and models for tracking and reuse matches the main goal of logging.

3. Final Answer:

   To save files and models for tracking and reuse -> Option D

4. Quick Check:

   Logging = Save and track work [OK]

Hint: Logging means saving work for later use [OK]

Common Mistakes:

• Thinking logging deletes models
• Confusing logging with speeding training
• Assuming logging creates new data

2. Which of the following is the correct syntax to log a file artifact using MLflow?

easy

A. mlflow.log_model('path/to/file.txt')

B. mlflow.log_artifact('path/to/file.txt')

C. mlflow.log_artifacts('path/to/file.txt')

D. mlflow.log('path/to/file.txt')

Solution

1. Step 1: Recall MLflow function for logging files

   The correct function is mlflow.log_artifact() for single files.

2. Step 2: Check syntax correctness

   mlflow.log_artifact('path/to/file.txt') matches the correct syntax.

3. Final Answer:

   mlflow.log_artifact('path/to/file.txt') -> Option B

4. Quick Check:

   Single file logging = log_artifact() [OK]

Hint: Use log_artifact() for single files [OK]

Common Mistakes:

• Using log_model() for files
• Using plural log_artifacts() incorrectly
• Using generic log() function

3. What will be the output of this code snippet?

import mlflow
with mlflow.start_run():
    mlflow.log_artifact('data.csv')
    mlflow.log_model(model, 'model')
print('Run finished')

medium

A. Error because model is not defined

B. Run finished printed; artifacts and model logged in current run

C. No output; code hangs

D. Run finished printed; but nothing logged

Solution

1. Step 1: Analyze the code snippet

   The code tries to log a file and a model inside a run.

2. Step 2: Check for errors

   The variable 'model' is not defined, so mlflow.log_model(model, 'model') causes a NameError.

3. Final Answer:

   Error because model is not defined -> Option A

4. Quick Check:

   Undefined variable causes error [OK]

Hint: Check if variables are defined before logging [OK]

Common Mistakes:

• Assuming code runs without defining model
• Thinking print means success
• Ignoring variable definitions

4. You run this code but no artifacts appear in MLflow UI:

mlflow.log_artifact('output.txt')

What is the most likely reason?

medium

A. log_artifact() only works inside a run

B. The file output.txt does not exist

C. No active MLflow run was started

D. MLflow server is down

Solution

1. Step 1: Understand MLflow run context

   Logging artifacts requires an active run to group logs.

2. Step 2: Identify missing run

   Without mlflow.start_run(), logs are not saved properly.

3. Final Answer:

   No active MLflow run was started -> Option C

4. Quick Check:

   Logging needs active run [OK]

Hint: Always start a run before logging [OK]

Common Mistakes:

• Assuming logging works without a run
• Ignoring file existence
• Blaming server without checking run

5. You want to log multiple files and a trained model in one MLflow run. Which code snippet correctly does this?

hard

A. with mlflow.start_run():
mlflow.log_artifact('file1.txt')
mlflow.log_artifact('file2.txt')
mlflow.log_model(model, 'model')

B. mlflow.log_artifact(['file1.txt', 'file2.txt'])
mlflow.log_model(model, 'model')

C. with mlflow.start_run():
mlflow.log_artifacts(['file1.txt', 'file2.txt'])
mlflow.log_model(model, 'model')

D. mlflow.start_run()
mlflow.log_artifact('file1.txt')
mlflow.log_artifacts('file2.txt')
mlflow.log_model(model, 'model')
mlflow.end_run()

Solution

1. Step 1: Identify correct way to log multiple files

   For multiple individual files, call mlflow.log_artifact() for each file.

2. Step 2: Confirm run context and model logging

   Using with mlflow.start_run(): ensures proper context; logs each file and the model correctly.

3. Final Answer:

   with mlflow.start_run(): mlflow.log_artifact('file1.txt') mlflow.log_artifact('file2.txt') mlflow.log_model(model, 'model') -> Option A

4. Quick Check:

   Multiple files = log_artifact() for each inside run [OK]

Hint: Call log_artifact() for each file inside a run [OK]

Common Mistakes:

• Passing list to log_artifacts()
• Not using a run context
• Using log_artifacts() on single file