Model versioning in ML Python - Model Pipeline Trace

Model Pipeline - Model versioning

Model versioning helps keep track of different versions of a machine learning model as it improves. It is like saving different drafts of a school essay to see which one works best.

Data Flow - 7 Stages

1Data Collection

1000 rows x 10 columns→Collect raw data for training→1000 rows x 10 columns

Rows of customer info with 10 features each

↓

2Data Preprocessing

1000 rows x 10 columns→Clean and normalize data→1000 rows x 10 columns

Normalized numbers, missing values filled

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3Feature Engineering

1000 rows x 10 columns→Create new features from existing data→1000 rows x 12 columns

Added two new columns combining features

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4Model Training v1

1000 rows x 12 columns→Train first model version→Model v1 saved

Model v1 with accuracy 75%

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5Model Training v2

1000 rows x 12 columns→Train improved model version→Model v2 saved

Model v2 with accuracy 80%

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6Model Evaluation

Test set 200 rows x 12 columns→Evaluate model versions→Performance metrics for each version

Model v1 accuracy 75%, Model v2 accuracy 80%

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7Model Deployment

Selected model version→Deploy chosen model to production→Model serving live predictions

Model v2 deployed for real-time use

Training Trace - Epoch by Epoch

Loss
0.65 |****
0.50 |***
0.40 |**
0.35 |**
0.30 |*
Epochs ->

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Starting training, loss high, accuracy low
2	0.50	0.70	Loss decreased, accuracy improved
3	0.40	0.75	Model learning well
4	0.35	0.78	Loss continues to drop, accuracy rising
5	0.30	0.80	Training converging, good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer 1 (ReLU)

Layer 3: Hidden Layer 2 (ReLU)

Layer 4: Output Layer (Softmax)

Layer 5: Prediction

Model Quiz - 3 Questions

Why do we save multiple versions of a model during training?

A To compare performance and pick the best one

B To confuse users with many models

C Because models get lost easily

D To use all versions at the same time

Key Insight

Model versioning helps track improvements by saving different model versions. Watching training loss go down and accuracy go up shows the model is learning. Softmax at the output turns numbers into probabilities, making predictions easy to understand.

Practice

1. What is the main purpose of model versioning in machine learning?

easy

A. To speed up the training process

B. To save different copies of a model as it improves

C. To reduce the size of the dataset

D. To increase the number of features automatically

Solution

1. Step 1: Understand the concept of model versioning

   Model versioning means saving multiple copies of a model at different stages to track improvements.

2. Step 2: Identify the main goal

   The main goal is to keep versions so you can compare, update, or reproduce models easily.

3. Final Answer:

   To save different copies of a model as it improves -> Option B

4. Quick Check:

   Model versioning = saving model copies [OK]

Hint: Think: Why save multiple models? To track changes [OK]

Common Mistakes:

• Confusing versioning with speeding training
• Thinking it reduces dataset size
• Assuming it adds features automatically

2. Which of the following is the correct way to save a model version in Python using joblib?

easy

A. joblib.store(model, 'model_v1.pkl')

B. joblib.save(model, 'model_v1.pkl')

C. joblib.dump(model, 'model_v1.pkl')

D. joblib.write(model, 'model_v1.pkl')

Solution

1. Step 1: Recall joblib syntax for saving models

   The correct function to save a model with joblib is dump.

2. Step 2: Match the correct function and filename

   Using joblib.dump(model, 'model_v1.pkl') saves the model correctly.

3. Final Answer:

   joblib.dump(model, 'model_v1.pkl') -> Option C

4. Quick Check:

   joblib save function = dump [OK]

Hint: Remember: joblib uses dump to save, load to load [OK]

Common Mistakes:

• Using save instead of dump
• Using store or write which don't exist
• Confusing load and dump functions

3. Consider this Python code snippet for saving model versions:

import joblib

model_v1 = {'accuracy': 0.8}
model_v2 = {'accuracy': 0.85}

joblib.dump(model_v1, 'model_v1.pkl')
joblib.dump(model_v2, 'model_v2.pkl')

loaded_model = joblib.load('model_v2.pkl')
print(loaded_model['accuracy'])

What will be the printed output?

medium

A. 0.8

B. model_v2.pkl

C. Error: file not found

D. 0.85

Solution

1. Step 1: Understand the saving and loading process

   Two models are saved with different filenames: 'model_v1.pkl' and 'model_v2.pkl'.

2. Step 2: Identify which model is loaded and printed

   The code loads 'model_v2.pkl' and prints its accuracy value, which is 0.85.

3. Final Answer:

   0.85 -> Option D

4. Quick Check:

   Loaded model accuracy = 0.85 [OK]

Hint: Check which file is loaded before printing [OK]

Common Mistakes:

• Printing accuracy of model_v1 instead of model_v2
• Confusing filename with accuracy value
• Assuming loading causes error

4. You have this code to save model versions but it throws an error:

import joblib

model = {'accuracy': 0.9}
joblib.dump(model, model_v1.pkl)

What is the error and how to fix it?

medium

A. model_v1.pkl is not quoted as a string; add quotes

B. joblib.dump requires three arguments; add one more

C. model must be a trained model object, not a dict

D. joblib module is not imported correctly

Solution

1. Step 1: Identify the syntax error in the filename

   The filename model_v1.pkl is used without quotes, so Python treats it as a variable, causing a NameError.

2. Step 2: Fix by adding quotes around the filename

   Change to joblib.dump(model, 'model_v1.pkl') to pass a string filename.

3. Final Answer:

   model_v1.pkl is not quoted as a string; add quotes -> Option A

4. Quick Check:

   Filename must be a string [OK]

Hint: Always quote filenames as strings in save/load functions [OK]

Common Mistakes:

• Forgetting quotes around filename
• Adding unnecessary extra arguments
• Thinking model type causes error here

5. You want to keep track of multiple model versions with their training accuracy and date. Which approach below best supports clear model versioning and easy retrieval?

hard

A. Save models with filenames like 'model_2023-06-01_acc0.85.pkl' and keep a CSV log with version info

B. Overwrite the same file 'model.pkl' every time you train a new model

C. Save models without any naming pattern and search manually later

D. Save only the best model and delete all others

Solution

1. Step 1: Understand the need for clear versioning

   Clear filenames with date and accuracy help identify model versions easily.

2. Step 2: Use a CSV log to track versions and metadata

   Keeping a CSV log allows easy retrieval and comparison of models.

3. Final Answer:

   Save models with filenames like 'model_2023-06-01_acc0.85.pkl' and keep a CSV log with version info -> Option A

4. Quick Check:

   Clear naming + log = best versioning [OK]

Hint: Use descriptive filenames plus a log file for tracking [OK]

Common Mistakes:

• Overwriting files losing old versions
• Saving without naming pattern causing confusion
• Deleting older models losing history