Apache Airflow for ML orchestration in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Apache Airflow used for in ML projects?

Apache Airflow helps automate and schedule tasks in machine learning workflows, like data preparation, model training, and deployment.

beginner

What is a DAG in Apache Airflow?

A DAG (Directed Acyclic Graph) is a set of tasks with dependencies that Airflow runs in order, representing the workflow steps.

intermediate

How does Airflow ensure tasks run in the correct order?

Airflow uses dependencies defined in the DAG to run tasks only after their upstream tasks have completed successfully.

beginner

Name two common operators used in Airflow for ML workflows.

PythonOperator to run Python code and BashOperator to run shell commands are common operators in ML workflows.

intermediate

Why is Airflow useful for retraining ML models regularly?

Airflow can schedule retraining tasks automatically at set intervals, ensuring models stay updated without manual work.

What does DAG stand for in Apache Airflow?

A Dynamic Application Gateway

B Data Analysis Group

C Distributed Automation Grid

D Directed Acyclic Graph

DAG means Directed Acyclic Graph, which defines the order of tasks in Airflow.

Which Airflow component defines the workflow steps and their order?

A Operator

B DAG

C Task Instance

D Scheduler

The DAG defines the workflow steps and their dependencies.

Which operator would you use to run a Python function in Airflow?

A PythonOperator

B BashOperator

C EmailOperator

D DockerOperator

PythonOperator runs Python code inside Airflow tasks.

How does Airflow handle task failures by default?

A It stops the entire workflow immediately

B It ignores failures and continues

C It retries the task a set number of times

D It sends an email but continues

Airflow retries failed tasks based on retry settings before marking them as failed.

What is a key benefit of using Airflow for ML model retraining?

A Scheduling retraining automatically

B Manual triggering of retraining

C Replacing the need for data scientists

D Automatically improving model accuracy

Airflow schedules retraining tasks automatically to keep models updated.

Explain how Apache Airflow helps manage machine learning workflows.

Think about how Airflow organizes and runs steps like data prep and model training.

Describe the role of operators in Airflow and name two used in ML pipelines.

Operators are like tools Airflow uses to do work.

Practice

1. What is the main purpose of Apache Airflow in ML orchestration?

easy

A. To store large datasets for ML training

B. To write ML model code in Python

C. To visualize ML model performance metrics

D. To automate and schedule ML workflows as directed tasks

Solution

1. Step 1: Understand Airflow's role

   Apache Airflow is designed to automate workflows by scheduling and running tasks in order.

2. Step 2: Differentiate from other ML tools

   It does not store data, visualize metrics, or write model code but manages task execution.

3. Final Answer:

   To automate and schedule ML workflows as directed tasks -> Option D

4. Quick Check:

   Airflow = workflow automation [OK]

Hint: Airflow schedules tasks, not data or model code [OK]

Common Mistakes:

• Confusing Airflow with data storage tools
• Thinking Airflow writes ML model code
• Assuming Airflow visualizes model metrics

2. Which of the following is the correct way to define a DAG in Apache Airflow using Python?

easy

A. dag = DAG('my_dag', run_every='daily')

B. dag = DAG('my_dag', schedule_interval='@daily')

C. dag = DAG('my_dag', interval='daily')

D. dag = DAG('my_dag', schedule='daily')

Solution

1. Step 1: Recall DAG initialization syntax

   The correct parameter to set schedule is schedule_interval, not run_every, interval, or schedule.

2. Step 2: Verify the example

   dag = DAG('my_dag', schedule_interval='@daily') is the standard syntax to schedule daily runs.

3. Final Answer:

   dag = DAG('my_dag', schedule_interval='@daily') -> Option B

4. Quick Check:

   Use schedule_interval to set DAG timing [OK]

Hint: Use schedule_interval to set DAG timing [OK]

Common Mistakes:

• Using incorrect parameter names like run_every
• Confusing schedule_interval with schedule
• Forgetting to use quotes around '@daily'

3. Given the following Airflow DAG snippet, what will be the order of task execution?

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def task_a():
    print('Task A')

def task_b():
    print('Task B')

def task_c():
    print('Task C')

dag = DAG('example_dag', start_date=datetime(2024, 1, 1), schedule_interval='@once')

t1 = PythonOperator(task_id='a', python_callable=task_a, dag=dag)
t2 = PythonOperator(task_id='b', python_callable=task_b, dag=dag)
t3 = PythonOperator(task_id='c', python_callable=task_c, dag=dag)

t1 >> t2 >> t3

medium

A. Task A, then Task B, then Task C

B. Task C, then Task B, then Task A

C. Task A, Task B, and Task C run in parallel

D. Task B, then Task A, then Task C

Solution

1. Step 1: Understand task dependencies

   The operator chaining t1 >> t2 >> t3 means t1 runs first, then t2, then t3.

2. Step 2: Confirm execution order

   Tasks print in order: Task A, Task B, Task C.

3. Final Answer:

   Task A, then Task B, then Task C -> Option A

4. Quick Check:

   Operator chaining sets task order [OK]

Hint: >> means run left task before right task [OK]

Common Mistakes:

• Assuming tasks run in parallel without dependencies
• Misreading the >> operator order
• Confusing task IDs with execution order

4. You wrote this Airflow DAG code but get an error: TypeError: DAG.__init__() got an unexpected keyword argument 'start'
What is the likely cause?

dag = DAG('my_dag', start='2024-01-01', schedule_interval='@daily')

medium

A. The parameter should be start_date, not start

B. The schedule_interval value '@daily' is invalid

C. DAG name cannot be 'my_dag'

D. Missing import for datetime module

Solution

1. Step 1: Identify incorrect parameter

   The error says start is unexpected; Airflow expects start_date.

2. Step 2: Confirm correct parameter usage

   Replacing start with start_date fixes the error.

3. Final Answer:

   The parameter should be start_date, not start -> Option A

4. Quick Check:

   Use start_date, not start [OK]

Hint: Use start_date, not start, for DAG start time [OK]

Common Mistakes:

• Using 'start' instead of 'start_date'
• Assuming '@daily' is invalid schedule
• Ignoring error message details

5. You want to create an Airflow DAG that runs an ML training task only if data preprocessing succeeded. Which Airflow feature should you use to enforce this dependency?

hard

A. Schedule both tasks to run at the same time

B. Use Airflow Variables to store task status

C. Set task dependencies using >> operator between preprocessing and training tasks

D. Write a single Python function combining both tasks

Solution

1. Step 1: Understand task dependency in Airflow

   Airflow uses task dependencies to control execution order, ensuring one task runs after another succeeds.

2. Step 2: Apply dependency operator

   Using the >> operator sets the training task to run only after preprocessing completes successfully.

3. Final Answer:

   Set task dependencies using >> operator between preprocessing and training tasks -> Option C

4. Quick Check:

   Use >> to enforce task order [OK]

Hint: Use >> to link tasks in order [OK]

Common Mistakes:

• Thinking Variables control task order
• Scheduling tasks simultaneously without dependencies
• Combining tasks loses modularity and control