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

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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

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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]"}

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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.