ML lifecycle stages in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to name the first stage of the ML lifecycle.

MLOps

stage = "[1]"  # First stage in ML lifecycle

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

ADeployment

BData Collection

CMonitoring

DModel Training

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'Model Training' as the first stage.

Confusing 'Deployment' with the start of the process.

The first stage in the ML lifecycle is Data Collection. This is where raw data is gathered for training the model.

2fill in blank

medium

Complete the code to name the stage where the model learns from data.

MLOps

stage = "[1]"  # Stage where model learns

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

AModel Training

BDeployment

CEvaluation

DData Preprocessing

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'Evaluation' instead of training.

Confusing 'Deployment' with training.

The Model Training stage is where the machine learning model learns patterns from the data.

3fill in blank

hard

Fix the error in naming the stage where the model's performance is checked.

MLOps

stage = "[1]"  # Stage to check model performance

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

AData Collection

BDeployment

CTraining

DEvaluation

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Training' instead of 'Evaluation'.

Confusing 'Deployment' with evaluation.

The Evaluation stage is where the model's accuracy and performance are tested using test data.

4fill in blank

hard

Fill both blanks to name the stages for preparing data and putting the model into use.

MLOps

stages = ["[1]", "[2]"]  # Data prep and model use stages

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

AData Preprocessing

BModel Training

CDeployment

DEvaluation

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up 'Evaluation' with 'Deployment'.

Choosing 'Training' for data preparation.

Data Preprocessing is preparing data for training, and Deployment is putting the trained model into real use.

5fill in blank

hard

Fill all three blanks to complete the dictionary showing model name, accuracy, and status.

MLOps

model_info = {"name": "[1]", "accuracy": [2], "status": "[3]"}

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

ARandomForest

B0.92

Cdeployed

DSVM

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'SVM' instead of 'RandomForest' for the model name.

Putting accuracy as a string instead of a number.

The model is named RandomForest, has an accuracy of 0.92, and its status is deployed.

Practice

1. Which stage in the ML lifecycle involves collecting and preparing data for training?

easy

A. Model Training

B. Data Preparation

C. Model Monitoring

D. Model Deployment

Solution

1. Step 1: Understand the role of data in ML lifecycle

   Data must be collected and cleaned before training a model.

2. Step 2: Identify the stage focused on data tasks

   Data Preparation is the stage where data is gathered and made ready for training.

3. Final Answer:

   Data Preparation -> Option B

4. Quick Check:

   Data Preparation = Collecting and cleaning data [OK]

Hint: Data tasks happen before training starts [OK]

Common Mistakes:

• Confusing deployment with data tasks
• Thinking monitoring includes data cleaning
• Mixing training with data preparation

2. Which of the following is the correct order of stages in a typical ML lifecycle?

easy

A. Data Preparation -> Model Training -> Model Deployment -> Model Monitoring

B. Model Deployment -> Model Training -> Data Preparation -> Model Monitoring

C. Model Training -> Data Preparation -> Model Deployment -> Model Monitoring

D. Model Monitoring -> Model Deployment -> Model Training -> Data Preparation

Solution

1. Step 1: Recall the logical flow of ML lifecycle stages

   First, data is prepared, then the model is trained, followed by deployment and monitoring.

2. Step 2: Match the correct sequence from options

   Data Preparation -> Model Training -> Model Deployment -> Model Monitoring correctly lists the stages in order: Data Preparation -> Model Training -> Model Deployment -> Model Monitoring.

3. Final Answer:

   Data Preparation -> Model Training -> Model Deployment -> Model Monitoring -> Option A

4. Quick Check:

   Correct stage order = Data Preparation -> Model Training -> Model Deployment -> Model Monitoring [OK]

Hint: Remember: Prepare data before training [OK]

Common Mistakes:

• Mixing deployment before training
• Starting with monitoring instead of data
• Incorrect stage order

3. Consider this simplified ML lifecycle code snippet:

stages = ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']
for i, stage in enumerate(stages):
    print(f"Stage {i+1}: {stage}")

What will be the output of this code?

medium

A. Stage 1: Model Training
Stage 2: Data Preparation
Stage 3: Model Deployment
Stage 4: Model Monitoring

B. Stage 0: Data Preparation
Stage 1: Model Training
Stage 2: Model Deployment
Stage 3: Model Monitoring

C. Stage 1: Data Preparation
Stage 2: Model Training
Stage 3: Model Deployment
Stage 4: Model Monitoring

D. Stage 1: Data Preparation
Stage 2: Model Deployment
Stage 3: Model Training
Stage 4: Model Monitoring

Solution

1. Step 1: Understand enumerate behavior in the loop

   enumerate(stages) gives index and value starting at 0, but print uses i+1 for stage number.

2. Step 2: Check the order of stages printed

   The loop prints stages in list order with stage numbers 1 to 4 matching the list order.

3. Final Answer:

   Stage 1: Data Preparation Stage 2: Model Training Stage 3: Model Deployment Stage 4: Model Monitoring -> Option C

4. Quick Check:

   Index + 1 matches stage number [OK]

Hint: Remember enumerate starts at 0, add 1 for display [OK]

Common Mistakes:

• Confusing index starting at 0
• Mixing stage order in output
• Printing wrong stage names

4. You have this ML lifecycle stage list:

stages = ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']
stages.remove('Model Training')
print(stages)

What is the output after running this code?

medium

A. ['Data Preparation', 'Model Deployment', 'Model Monitoring']

B. ['Model Training', 'Model Deployment', 'Model Monitoring']

C. ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']

D. Error: 'remove' method not found

Solution

1. Step 1: Understand what stages.remove('Model Training') does

   This removes the first occurrence of 'Model Training' from the list.

2. Step 2: Check the list after removal

   The list now excludes 'Model Training', leaving the other three stages.

3. Final Answer:

   ['Data Preparation', 'Model Deployment', 'Model Monitoring'] -> Option A

4. Quick Check:

   Remove deletes specified item from list [OK]

Hint: remove() deletes the exact item from list [OK]

Common Mistakes:

• Expecting an error from remove()
• Thinking remove deletes by index
• Not updating the list after removal

5. A team wants to automate retraining their ML model when data changes. Which two ML lifecycle stages must be combined in a pipeline to achieve this?

hard

A. Data Preparation and Model Monitoring

B. Model Deployment and Model Monitoring

C. Model Training and Model Deployment

D. Data Preparation and Model Training

Solution

1. Step 1: Identify stages involved in retraining after data changes

   Retraining requires fresh data preparation and then training the model again.

2. Step 2: Select stages that automate retraining

   Data Preparation and Model Training together form the pipeline for retraining.

3. Final Answer:

   Data Preparation and Model Training -> Option D

4. Quick Check:

   Retrain = Prepare data + Train model [OK]

Hint: Retraining needs fresh data prep plus training [OK]

Common Mistakes:

• Confusing deployment with retraining
• Thinking monitoring triggers retraining alone
• Ignoring data preparation before training