Model registry in ML Python - Model Pipeline Trace

Model Pipeline - Model registry

A model registry is a system that helps keep track of different versions of machine learning models. It stores models, their metadata, and performance so teams can easily find, compare, and deploy the best model.

Data Flow - 4 Stages

1Model Training

10000 rows x 20 columns→Train model on dataset→Trained model object

Model trained on customer data to predict churn

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2Model Registration

Trained model object→Save model with metadata (version, metrics, parameters)→Model stored in registry with unique ID

Model v1.0 saved with accuracy 0.85

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3Model Versioning

Multiple model versions→Track and compare different model versions→List of model versions with metadata

Model v1.0, v1.1, v2.0 with respective metrics

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4Model Deployment

Selected model version→Deploy model to production environment→Model serving predictions

Model v2.0 deployed to web service

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning patterns
2	0.50	0.72	Loss decreases, accuracy improves
3	0.40	0.80	Model converging well
4	0.35	0.83	Further improvement
5	0.32	0.85	Training stabilizes

Prediction Trace - 4 Layers

Layer 1: Input Data

Layer 2: Feature Scaling

Layer 3: Model Prediction

Layer 4: Thresholding

Model Quiz - 3 Questions

What is the main purpose of a model registry?

A To store and manage different versions of machine learning models

B To train machine learning models faster

C To collect raw data for training

D To visualize model predictions

Key Insight

A model registry helps teams keep track of many models and their versions, making it easier to pick the best one for deployment. Watching training loss go down and accuracy go up shows the model is learning well. Prediction steps transform raw data into a final decision the model makes.