Logging artifacts and models in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to log a model artifact using MLflow.

MLOps

import mlflow
mlflow.[1].log_model(model, "model")

Drag options to blanks, or click blank then click option'

Alog

Btracking

Cmodels

Dpyfunc

Attempts:

3 left

💡 Hint

Common Mistakes

Using mlflow.log instead of mlflow.pyfunc.log_model

Trying to log model directly without specifying the module

Use mlflow.pyfunc.log_model to log a model artifact in MLflow.

2fill in blank

medium

Complete the code to log an artifact file in MLflow.

MLOps

with mlflow.start_run():
    mlflow.[1]("output.txt")

Drag options to blanks, or click blank then click option'

Atracking

Blog

Clog_artifact

Dmodels

Attempts:

3 left

💡 Hint

Common Mistakes

Using mlflow.log_param instead of mlflow.log_artifact

Trying to log artifact outside a run context

Use mlflow.log_artifact to log files as artifacts.

3fill in blank

hard

Fix the error in the code to log a model with MLflow.

MLOps

mlflow.[1].log_model(model, "model")

Drag options to blanks, or click blank then click option'

Asklearn

Bmodels

Cpyfunc

Dartifacts

Attempts:

3 left

💡 Hint

Common Mistakes

Calling mlflow.log_model directly without module

Using mlflow.artifacts to log models

Use mlflow.sklearn.log_model to log scikit-learn models properly.

4fill in blank

hard

Fill both blanks to log a model and an artifact inside a run.

MLOps

with mlflow.[1]():
    mlflow.sklearn.[2](model, "model")

Drag options to blanks, or click blank then click option'

Astart_run

Blog_model

Clog_artifact

Dend_run

Attempts:

3 left

💡 Hint

Common Mistakes

Not starting a run before logging

Using log_artifact to log models

Use mlflow.start_run() to start a run and mlflow.sklearn.log_model to log the model.

5fill in blank

hard

Fill all three blanks to log a model, an artifact, and end the run properly.

MLOps

mlflow.[1]()
mlflow.sklearn.[2](model, "model")
mlflow.[3]("output.txt")

Drag options to blanks, or click blank then click option'

Astart_run

Blog_model

Clog_artifact

Dend_run

Attempts:

3 left

💡 Hint

Common Mistakes

Logging without starting a run

Confusing log_model and log_artifact methods

Start the run with start_run(), log the model and artifact, then end the run if needed.

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