ML workflow (collect, prepare, train, evaluate, deploy) in ML Python

Introduction

ML workflow helps you build smart programs step-by-step. It makes sure your model learns well and works in real life.

When you want to teach a computer to recognize pictures of cats and dogs.

When you need to predict tomorrow's weather using past data.

When you want to sort emails into spam and not spam automatically.

When you want to improve a recommendation system for movies or products.

When you want to create a chatbot that answers customer questions.

Syntax

ML Python

1. Collect data
2. Prepare data
3. Train model
4. Evaluate model
5. Deploy model

Each step builds on the previous one to create a working ML system.

Skipping steps can cause poor results or errors.

Examples

This example shows a simple image classification workflow.

ML Python

1. Collect data: Gather images of cats and dogs
2. Prepare data: Resize and label images
3. Train model: Teach model to tell cats from dogs
4. Evaluate model: Check accuracy on new images
5. Deploy model: Use model in a phone app

This example shows a regression workflow for weather prediction.

ML Python

1. Collect data: Download weather records
2. Prepare data: Fill missing values and normalize
3. Train model: Use past data to predict temperature
4. Evaluate model: Measure prediction error
5. Deploy model: Add model to a weather website

Sample Model

This code shows a full ML workflow using the Iris flower dataset. It collects data, prepares it by splitting and scaling, trains a logistic regression model, evaluates accuracy, and simulates deployment by printing predictions.

ML Python

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 1. Collect data
iris = load_iris()
X, y = iris.data, iris.target

# 2. Prepare data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 3. Train model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# 4. Evaluate model
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

# 5. Deploy model
# (In real life, save model and use in app; here we just print predictions)
print("Sample predictions:", predictions[:5])

Important Notes

Good data is key: collecting and preparing data well improves results a lot.

Always check your model with new data to avoid surprises.

Deployment means making your model useful outside your computer, like in apps or websites.

Summary

ML workflow has five main steps: collect, prepare, train, evaluate, and deploy.

Each step is important to build a model that works well in real life.

Following the workflow helps you create smart programs step-by-step.

Practice

1. Which step in the ML workflow involves gathering raw data from various sources?

easy

A. Deploy

B. Train

C. Evaluate

D. Collect

Solution

1. Step 1: Understand the meaning of each ML workflow step

   Collect means to gather raw data needed for the model.

2. Step 2: Match the step to the description

   Since gathering data is the first step, it matches 'Collect'.

3. Final Answer:

   Collect -> Option D

4. Quick Check:

   Gathering data = Collect [OK]

Hint: Collect means get data first before anything else [OK]

Common Mistakes:

• Confusing 'Train' with data gathering
• Thinking 'Evaluate' means collecting data
• Mixing 'Deploy' with data collection

2. Which of the following is the correct order of the ML workflow steps?

easy

A. Collect, Prepare, Train, Evaluate, Deploy

B. Train, Collect, Prepare, Deploy, Evaluate

C. Prepare, Collect, Train, Evaluate, Deploy

D. Deploy, Evaluate, Train, Prepare, Collect

Solution

1. Step 1: Recall the standard ML workflow order

   The correct order is: Collect data, Prepare data, Train model, Evaluate model, Deploy model.

2. Step 2: Compare each option to the correct order

   Only Collect, Prepare, Train, Evaluate, Deploy matches this exact sequence.

3. Final Answer:

   Collect, Prepare, Train, Evaluate, Deploy -> Option A

4. Quick Check:

   Correct ML workflow order = Collect, Prepare, Train, Evaluate, Deploy [OK]

Hint: Remember: Collect first, then Prepare, Train, Evaluate, Deploy [OK]

Common Mistakes:

• Mixing up Collect and Prepare order
• Putting Deploy before Evaluate
• Starting with Train before data collection

3. Given this Python code snippet for training a model, what will be printed after evaluation?

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

medium

A. Accuracy: 0.98

B. Accuracy: 0.85

C. Accuracy: 0.70

D. Accuracy: 1.00

Solution

1. Step 1: Understand the code flow

   The code loads iris data, splits it, trains a Decision Tree, predicts, and calculates accuracy.

2. Step 2: Recall typical Decision Tree accuracy on iris test split

   With random_state=42 and 30% test size, accuracy is 0.98.

3. Final Answer:

   Accuracy: 0.98 -> Option A

4. Quick Check:

   Decision Tree on iris test = 0.98 accuracy [OK]

Hint: Decision Trees on iris usually score 98% accuracy [OK]

Common Mistakes:

• Assuming accuracy is always 1.00
• Confusing train and test accuracy
• Ignoring random_state effect on split

4. You wrote this code to train a model but get an error. What is the main problem?

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
print(model.predict(X_test))

Assuming X_train, y_train, and X_test are defined but you forgot to prepare data properly.

medium

A. Model was not imported correctly

B. Data was not scaled or cleaned before training

C. Predict method is called before fit

D. X_test has different features than X_train

Solution

1. Step 1: Analyze the error cause

   Logistic Regression often requires data preparation like scaling or cleaning.

2. Step 2: Identify missing preparation step

   Since data preparation is missing, model may fail or give poor results.

3. Final Answer:

   Data was not scaled or cleaned before training -> Option B

4. Quick Check:

   Missing data prep causes training issues [OK]

Hint: Always prepare data before training models [OK]

Common Mistakes:

• Thinking import error caused failure
• Assuming predict before fit error
• Ignoring feature mismatch possibility

5. You want to deploy a model that predicts house prices. Which step should you do right after evaluating the model's accuracy?

hard

A. Collect more data

B. Prepare the data again

C. Deploy the model to a real environment

D. Train the model with new parameters

Solution

1. Step 1: Understand the ML workflow sequence

   After evaluating the model, the next step is to deploy it if accuracy is good.

2. Step 2: Match the step after evaluation

   Deploy means putting the model into real use for predictions.

3. Final Answer:

   Deploy the model to a real environment -> Option C

4. Quick Check:

   Evaluation done, next is Deploy [OK]

Hint: Deploy comes right after evaluation if model is good [OK]

Common Mistakes:

• Repeating data collection unnecessarily
• Preparing data again without new data
• Retraining without evaluation feedback