Kubeflow Pipelines overview in MLOps - Mini Project: Build & Apply

Kubeflow Pipelines Overview

📖 Scenario: You are working as a data scientist who wants to automate a simple machine learning workflow using Kubeflow Pipelines. This will help you run your steps in order and track results easily.

🎯 Goal: Build a basic Kubeflow Pipeline with three steps: data preparation, model training, and model evaluation. You will define each step as a Python function, create pipeline components, and then assemble them into a pipeline.

📋 What You'll Learn

Create Python functions for each pipeline step

Convert functions to Kubeflow pipeline components

Define a pipeline that connects the components in order

Compile and run the pipeline to see the output

💡 Why This Matters

🌍 Real World

Kubeflow Pipelines help automate machine learning workflows so data scientists can run experiments easily and track results.

💼 Career

Understanding Kubeflow Pipelines is important for MLOps engineers and data scientists working on production ML systems.

1

Create Python functions for pipeline steps

Write three Python functions named prepare_data, train_model, and evaluate_model. Each function should print a simple message: "Preparing data...", "Training model...", and "Evaluating model..." respectively.

MLOps

# Define the three functions below
# Your code here

Hint

Define each function with def and use print() inside to show the step message.

2

Convert functions to Kubeflow pipeline components

Import kfp.components.create_component_from_func and create components named prepare_data_op, train_model_op, and evaluate_model_op from the functions prepare_data, train_model, and evaluate_model respectively.

MLOps

def prepare_data():
    print("Preparing data...")

def train_model():
    print("Training model...")

def evaluate_model():
    print("Evaluating model...")

# Import create_component_from_func and create components
# Your code here

Hint

Use create_component_from_func(function_name) to convert each function into a pipeline component.

3

Define the Kubeflow pipeline connecting components

Import kfp.dsl.pipeline and define a pipeline function named ml_pipeline. Inside it, create tasks by calling prepare_data_op(), then train_model_op(), and finally evaluate_model_op(). Make sure train_model_op runs after prepare_data_op, and evaluate_model_op runs after train_model_op.

MLOps

from kfp.components import create_component_from_func
from kfp.dsl import pipeline

def prepare_data():
    print("Preparing data...")

def train_model():
    print("Training model...")

def evaluate_model():
    print("Evaluating model...")

prepare_data_op = create_component_from_func(prepare_data)
train_model_op = create_component_from_func(train_model)
evaluate_model_op = create_component_from_func(evaluate_model)

# Define the pipeline function ml_pipeline
# Your code here

Hint

Use the @pipeline decorator and call components inside the pipeline function. Use task.after(other_task) to set order.

4

Compile and run the Kubeflow pipeline

Import kfp.Client and kfp.compiler. Compile the pipeline ml_pipeline to a file named ml_pipeline.yaml. Then create a kfp.Client instance and run the pipeline with the name ml-pipeline-run.

MLOps

from kfp import compiler, Client

# Compile the pipeline to ml_pipeline.yaml
# Create a Client instance
# Run the pipeline with name 'ml-pipeline-run'
# Your code here

Hint

Use compiler.Compiler().compile() to create the YAML file. Then use Client() and create_run_from_pipeline_func() to run the pipeline.

Practice

1. What is the main purpose of Kubeflow Pipelines in machine learning workflows?

easy

A. To store large datasets for training

B. To create user interfaces for ML models

C. To automate and manage ML workflows with clear, reusable steps

D. To replace Kubernetes as a container platform

Solution

1. Step 1: Understand Kubeflow Pipelines' role

   Kubeflow Pipelines are designed to automate ML workflows by defining clear steps that can be reused and tracked.

2. Step 2: Compare options with this role

   Options describing UI creation, data storage, and replacing Kubernetes do not match this role.

3. Final Answer:

   To automate and manage ML workflows with clear, reusable steps -> Option C

4. Quick Check:

   Automation of ML workflows [OK]

Hint: Kubeflow Pipelines automate ML steps, not UI or storage [OK]

Common Mistakes:

• Confusing Kubeflow Pipelines with data storage tools
• Thinking Kubeflow replaces Kubernetes
• Assuming it builds user interfaces

2. Which of the following is the correct way to define a step in a Kubeflow Pipeline using Python?

easy

A. def step():
return dsl.ContainerOp(name='step', image='python:3.8')

B. def step():
return dsl.ContainerOp(image='python:3.8')

C. def step():
return dsl.ContainerOp(name='step')

D. def step():
return dsl.ContainerOp(name='step', image='python:3.8', command=['python', 'script.py'])

Solution

1. Step 1: Understand ContainerOp usage

   ContainerOp requires at least a name, image, and usually a command to run the step's container.

2. Step 2: Check each option

   def step(): return dsl.ContainerOp(name='step', image='python:3.8', command=['python', 'script.py']) correctly includes name, image, and command. The version with name and image misses command. The version with only image misses name. The version with only name misses image and command.

3. Final Answer:

   def step():\n return dsl.ContainerOp(name='step', image='python:3.8', command=['python', 'script.py']) -> Option D

4. Quick Check:

   ContainerOp needs name, image, and command [OK]

Hint: ContainerOp needs name, image, and command to run [OK]

Common Mistakes:

• Omitting the command argument
• Not specifying the container image
• Missing the step name

3. Given this Kubeflow Pipeline snippet, what will be the output when the pipeline runs?

from kfp import dsl

@dsl.pipeline(name='Sample Pipeline')
def sample_pipeline():
    step1 = dsl.ContainerOp(
        name='echo-step',
        image='alpine',
        command=['echo', 'Hello Kubeflow']
    )

medium

A. The pipeline prints 'Hello Kubeflow' in the step logs

B. The pipeline fails because 'echo' is not a valid command

C. The pipeline prints 'Hello Kubeflow' on the console where pipeline is defined

D. The pipeline does nothing because no output is defined

Solution

1. Step 1: Understand ContainerOp execution

   The ContainerOp runs a container with the alpine image and executes the command 'echo Hello Kubeflow'. This prints to the container's standard output logs.

2. Step 2: Identify where output appears

   The output appears in the step logs inside Kubeflow Pipelines UI, not on the local console or nowhere.

3. Final Answer:

   The pipeline prints 'Hello Kubeflow' in the step logs -> Option A

4. Quick Check:

   ContainerOp command output = step logs [OK]

Hint: Container output appears in step logs, not local console [OK]

Common Mistakes:

• Thinking output appears on local console
• Assuming 'echo' command is invalid in alpine
• Believing pipeline does nothing without explicit output

4. You wrote this Kubeflow Pipeline step but it fails to run:

def step():
    return dsl.ContainerOp(name='step', image='python:3.8')

What is the most likely cause of the failure?

medium

A. Missing the command argument to specify what to run inside the container

B. The image 'python:3.8' does not exist

C. The step name cannot be 'step'

D. ContainerOp requires a volume to run

Solution

1. Step 1: Check ContainerOp requirements

   ContainerOp needs a command to run inside the container; without it, the container starts and exits immediately.

2. Step 2: Validate other options

   Image 'python:3.8' exists on Docker Hub, step name can be any string, and volume is optional.

3. Final Answer:

   Missing the command argument to specify what to run inside the container -> Option A

4. Quick Check:

   ContainerOp needs command to run [OK]

Hint: Always specify command in ContainerOp to avoid immediate exit [OK]

Common Mistakes:

• Assuming image is missing or invalid
• Thinking step name is restricted
• Believing volume is mandatory

5. You want to create a Kubeflow Pipeline that runs two steps sequentially: first preprocess data, then train a model. Which code snippet correctly defines this dependency?

hard

A. step1 = dsl.ContainerOp(name='preprocess', image='python:3.8', command=['python', 'preprocess.py'])
step2 = dsl.ContainerOp(name='train', image='python:3.8', command=['python', 'train.py'])
step1.after(step2)

B. step1 = dsl.ContainerOp(name='preprocess', image='python:3.8', command=['python', 'preprocess.py'])
step2 = dsl.ContainerOp(name='train', image='python:3.8', command=['python', 'train.py'])
step2.after(step1)

C. step1 = dsl.ContainerOp(name='preprocess', image='python:3.8', command=['python', 'preprocess.py'])
step2 = dsl.ContainerOp(name='train', image='python:3.8', command=['python', 'train.py'])
step1.before(step2)

D. step1 = dsl.ContainerOp(name='preprocess', image='python:3.8', command=['python', 'preprocess.py'])
step2 = dsl.ContainerOp(name='train', image='python:3.8', command=['python', 'train.py'])

Solution

1. Step 1: Understand step dependencies in Kubeflow Pipelines

   To run step2 after step1, use step2.after(step1) to set the order.

2. Step 2: Analyze each option

   step1 = dsl.ContainerOp(name='preprocess', image='python:3.8', command=['python', 'preprocess.py']) step2 = dsl.ContainerOp(name='train', image='python:3.8', command=['python', 'train.py']) step2.after(step1) correctly sets step2 to run after step1. Using step1.after(step2) reverses the order. Using step1.before(step2) calls a non-existent method. No dependency causes parallel execution.

3. Final Answer:

   step2.after(step1) -> Option B

4. Quick Check:

   Use step2.after(step1) for sequential steps [OK]

Hint: Use step2.after(step1) to run steps sequentially [OK]

Common Mistakes:

• Reversing the order with after()
• Using before() which does not exist
• Not setting any dependency causing parallel runs