ML workflow (collect, prepare, train, evaluate, deploy) in ML Python - Model Pipeline Trace

Model Pipeline - ML workflow (collect, prepare, train, evaluate, deploy)

This ML workflow shows how data is collected, prepared, used to train a model, evaluated for accuracy, and finally deployed for making predictions in real life.

Data Flow - 5 Stages

1Data Collection

0 rows x 0 columns→Gather raw data from sensors, files, or user input→1000 rows x 5 columns

Collected 1000 records of house features like size, rooms, age, location, price

↓

2Data Preparation

1000 rows x 5 columns→Clean data, fill missing values, encode categories→1000 rows x 5 columns

Filled missing prices, converted location names to numbers

↓

3Model Training

800 rows x 5 columns→Train model on 80% of data→Trained model with learned parameters

Used 800 houses to train a regression model to predict price

↓

4Model Evaluation

200 rows x 5 columns→Test model on 20% unseen data→Evaluation metrics like loss=0.15, accuracy=85%

Model predicted prices on 200 houses, error measured

↓

5Model Deployment

New house data 1 row x 5 columns→Use trained model to predict price→Predicted price value

Predicted price for a new house with given features

Training Trace - Epoch by Epoch

Loss
0.9 |*       
0.7 | **     
0.5 |  ***   
0.3 |    ****
0.1 |      *****
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning, loss high, accuracy low
2	0.60	0.70	Loss decreases, accuracy improves
3	0.40	0.78	Model learns important patterns
4	0.25	0.83	Loss continues to drop, accuracy rises
5	0.15	0.85	Model converges with good accuracy

Prediction Trace - 2 Layers

Layer 1: Input features

Layer 2: Model prediction

Model Quiz - 3 Questions

What happens during the Data Preparation stage?

A Deploying the model for predictions

B Training the model with data

C Cleaning and organizing raw data

D Collecting data from sensors

Key Insight

This workflow shows how raw data becomes a useful model through cleaning, training, and testing, then helps make real predictions in the world.

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