Training data pipeline automation in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file into a DataFrame using pandas.

MLOps

import pandas as pd
data = pd.read_csv([1])

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

Adata.csv

B'data.csv'

Cread.csv

Dcsv.read

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename

Using incorrect function names

To read a CSV file, the filename must be a string in quotes.

2fill in blank

medium

Complete the code to split data into training and testing sets using scikit-learn.

MLOps

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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

A0.2

B0.5

C2

D20

Attempts:

3 left

💡 Hint

Common Mistakes

Using integers instead of decimals

Setting test_size too large

test_size=0.2 means 20% of data is for testing, which is common practice.

3fill in blank

hard

Fix the error in the code to automate data scaling with StandardScaler.

MLOps

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.[1](X_train)

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

Ascale

Btransform

Cfit

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first

Using fit without transforming

fit_transform fits the scaler and transforms the data in one step.

4fill in blank

hard

Fill both blanks to create a pipeline that scales data and fits a logistic regression model.

MLOps

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
pipeline = Pipeline([('scaler', [1]()), ('model', [2]())])

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

AStandardScaler

BMinMaxScaler

CLogisticRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong scaler or model classes

Mixing up order of pipeline steps

The pipeline first scales data with StandardScaler, then fits LogisticRegression.

5fill in blank

hard

Fill all three blanks to automate training, prediction, and accuracy calculation.

MLOps

pipeline.fit([1], [2])
predictions = pipeline.predict([3])
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, predictions)

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

AX_train

By_train

CX_test

Dy_test

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up train and test sets

Using labels instead of features for prediction

Train with X_train and y_train, predict on X_test, then compare predictions to y_test.

Practice

1. What is the main benefit of automating a training data pipeline in machine learning?

easy

A. It saves time and reduces human errors during data preparation.

B. It makes the model training faster by using GPUs.

C. It increases the size of the training dataset automatically.

D. It guarantees 100% accuracy of the machine learning model.

Solution

1. Step 1: Understand the purpose of automation in data pipelines

   Automation helps by handling repetitive tasks consistently without manual intervention.

2. Step 2: Identify the key benefits of automation

   Automation saves time and reduces errors that happen when humans prepare data manually.

3. Final Answer:

   It saves time and reduces human errors during data preparation. -> Option A

4. Quick Check:

   Automation = saves time and reduces errors [OK]

Hint: Automation mainly saves time and avoids mistakes [OK]

Common Mistakes:

• Thinking automation speeds up model training directly
• Assuming automation increases dataset size automatically
• Believing automation guarantees perfect model accuracy

2. Which of the following is the correct Python syntax to define a simple function that automates a data cleaning step?

easy

A. clean_data(data) => data.dropna()

B. def clean_data(data):\n return data.dropna()

C. def clean_data(data):\nreturn data.dropna()

D. function clean_data(data) { return data.dropna() }

Solution

1. Step 1: Identify correct Python function syntax

   Python functions start with 'def', followed by name and parameters, then indented body.

2. Step 2: Check indentation and syntax correctness

   def clean_data(data):\n return data.dropna() uses correct indentation and syntax; others use wrong language syntax or missing indentation.

3. Final Answer:

   def clean_data(data):\n return data.dropna() -> Option B

4. Quick Check:

   Python function syntax = def + indent + return [OK]

Hint: Python functions need 'def' and proper indentation [OK]

Common Mistakes:

• Using JavaScript syntax in Python
• Missing indentation after function definition
• Using arrow functions which are not Python syntax

3. Consider this Python code snippet automating a data pipeline step:

def normalize(data):
    mean = data.mean()
    std = data.std()
    return (data - mean) / std

import pandas as pd
sample = pd.Series([10, 20, 30])
result = normalize(sample)
print(result.round(2))

What is the printed output?

medium

A. [ -1.0, 0.0, 1.0 ]

B. [ -1.22, 0.00, 1.22 ]

C. [ 10, 20, 30 ]

D. [ 0.0, 0.0, 0.0 ]

Solution

1. Step 1: Calculate mean and standard deviation of the sample

   Mean = (10+20+30)/3 = 20; Std deviation = 10 (pandas std() uses ddof=1 by default).

2. Step 2: Normalize each value and round to 2 decimals

   (10-20)/10 = -1.0, (20-20)/10=0.0, (30-20)/10 = 1.0

3. Final Answer:

   [ -1.0, 0.0, 1.0 ] -> Option A

4. Quick Check:

   Normalization = (value-mean)/std [OK]

Hint: Normalize by subtracting mean and dividing by std [OK]

Common Mistakes:

• Confusing standard deviation with variance
• Not rounding output
• Returning original data instead of normalized

4. You have this code snippet for automating data loading:

def load_data(file_path):
    data = pd.read_csv(file_path)
    return data

# Usage
dataset = load_data('data.csv')
print(dataset.head())

But it throws an error: NameError: name 'pd' is not defined. How do you fix it?

medium

A. Remove the function and read CSV directly.

B. Change 'pd.read_csv' to 'csv.read'.

C. Add 'import pandas as pd' at the top of the script.

D. Rename 'file_path' to 'filepath' in the function.

Solution

1. Step 1: Understand the error message

   NameError means 'pd' is not recognized because pandas was not imported.

2. Step 2: Fix by importing pandas with alias 'pd'

   Add 'import pandas as pd' at the top so 'pd.read_csv' works correctly.

3. Final Answer:

   Add 'import pandas as pd' at the top of the script. -> Option C

4. Quick Check:

   Import pandas as pd to use pd.read_csv [OK]

Hint: Always import pandas as pd before using pd functions [OK]

Common Mistakes:

• Changing function parameter names without reason
• Assuming csv module replaces pandas read_csv
• Removing function instead of fixing import

5. You want to automate a training data pipeline that:
1. Loads CSV data,
2. Cleans missing values,
3. Normalizes numeric columns,
4. Saves the processed data.

Which tool or approach best supports scheduling and monitoring this pipeline automatically?

hard

A. Using Excel macros to clean and normalize data.

B. Writing a single Python script and running it manually each time.

C. Training the model directly without data preprocessing.

D. Using Apache Airflow to create and schedule pipeline tasks.

Solution

1. Step 1: Identify requirements for automation and monitoring

   We need a tool that schedules tasks and tracks their success or failure.

2. Step 2: Evaluate options for pipeline automation

   Apache Airflow is designed for scheduling, monitoring, and managing workflows automatically.

3. Final Answer:

   Using Apache Airflow to create and schedule pipeline tasks. -> Option D

4. Quick Check:

   Airflow = scheduling + monitoring pipelines [OK]

Hint: Use Airflow for automated scheduling and monitoring [OK]

Common Mistakes:

• Running scripts manually instead of automating
• Using Excel which lacks automation for pipelines
• Skipping data preprocessing before training