CI/CD for ML pipelines in ML Python - Model Pipeline Trace

Model Pipeline - CI/CD for ML pipelines

This pipeline shows how Continuous Integration and Continuous Delivery (CI/CD) helps automate machine learning workflows. It ensures that data, code, and models are tested, trained, and deployed smoothly and reliably.

Data Flow - 6 Stages

1Data Collection

10000 rows x 10 columns→Gather raw data from sources→10000 rows x 10 columns

User activity logs with 10 features

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2Data Preprocessing

10000 rows x 10 columns→Clean data, handle missing values→10000 rows x 9 columns

Removed one column with too many missing values

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3Feature Engineering

10000 rows x 9 columns→Create new features and scale data→10000 rows x 12 columns

Added 3 new features from existing ones

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4Model Training

8000 rows x 12 columns→Train model on training set→Trained model object

Random Forest classifier trained on 8000 samples

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5Model Validation

2000 rows x 12 columns→Evaluate model on validation set→Validation metrics (accuracy, loss)

Accuracy: 0.85, Loss: 0.35

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6Model Deployment

Trained model object→Deploy model to production environment→Model API endpoint

REST API ready to serve predictions

Training Trace - Epoch by Epoch

Epoch 1: 0.65 #######
Epoch 2: 0.50 #####
Epoch 3: 0.40 ####
Epoch 4: 0.35 ###
Epoch 5: 0.33 ##

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate accuracy
2	0.50	0.72	Loss decreases and accuracy improves
3	0.40	0.80	Model converges with better performance
4	0.35	0.85	Training stabilizes with high accuracy
5	0.33	0.87	Slight improvement, model ready for validation

Prediction Trace - 4 Layers

Layer 1: Input Data

Layer 2: Feature Scaling

Layer 3: Model Prediction

Layer 4: Output Decision

Model Quiz - 3 Questions

What happens to the data shape after feature engineering?

A It increases in columns

B It decreases in rows

C It stays the same

D It becomes a model object

Key Insight

CI/CD pipelines automate the steps from data collection to model deployment, ensuring smooth updates and reliable machine learning models in production.