# Data Science

The Data Science layer provides infrastructure and tooling for machine learning workflows. It emphasizes reproducibility, governance, and integration with the broader data platform.

# Platform Capabilities

# Ad-Hoc Notebooks

Interactive development environment for data scientists:

Features:

Jupyter-compatible notebooks
Connection to data platform sources
Compute resources for experimentation
Collaborative editing capabilities

GitHub Integration:

Code stored in version control
Pull request workflows for review
CI/CD for notebook validation
Single source of truth for code

Best Practices:

Notebooks for exploration, scripts for production
Parameterize notebooks for reusability
Document assumptions and findings
Regular commits with meaningful messages

# Experiment Tracking

Record and compare model experiments:

Tracked metadata:

Model parameters and hyperparameters
Training data version
Performance metrics
Artifacts (models, plots, reports)

Benefits:

Reproducibility of results
Comparison across experiments
Audit trail for model decisions
Team visibility into work

# Model Lifecycle Management

Structured approach from concept to retirement:

Stages:

Stage	Activities
Conception	Problem definition, feasibility assessment
Development	Feature engineering, model training, validation
Deployment	Productionization, serving infrastructure
Observability	Monitoring, drift detection, performance tracking
Retraining	Scheduled or triggered model updates
Sunsetting	Deprecation and removal

# Blueprints for Business Reporting

Templates for common DS outputs:

Forecasting reports
Anomaly detection dashboards
Segmentation analyses
Model performance reports

# Model Lifecycle

# Conception

Before starting development:

Problem definition: Clear business objective
Success criteria: How will we measure success?
Data assessment: Is required data available?
Feasibility: Is ML the right approach?

# Development

Building and validating models:

Data Preparation:

Use platform data sources (Silver/Gold layers)
Document data transformations
Version training datasets
Handle data types consistently

Training:

Reproducible training pipelines
Hyperparameter tuning
Cross-validation
Experiment logging

Validation:

Hold-out test sets
Business metric validation
Bias and fairness checks
Edge case testing

# Deployment

Moving models to production:

Patterns:

Batch inference (scheduled predictions)
Real-time inference (API serving)
Embedded models (in-database scoring)

Requirements:

Version-controlled model artifacts
Documented dependencies
Health checks and monitoring
Rollback capability

# Observability

Monitoring production models:

Metrics to track:

Prediction latency
Error rates
Input data distribution
Output distribution
Business outcome correlation

Drift detection:

Data drift (input distribution changes)
Concept drift (relationship changes)
Performance degradation alerts

# Retraining

Keeping models current:

Triggers:

Scheduled (e.g., monthly)
Performance-based (degradation detected)
Data-based (significant new data available)

Process:

Automated pipeline execution
Validation against current model
Champion/challenger comparison
Approval for production update

# Sunsetting

Retiring models responsibly:

Identify replacement or alternative
Notify stakeholders
Deprecation period with warnings
Remove from production
Archive artifacts

# Feature Store

Centralized feature management (future capability):

# Concept

A feature store provides:

Reusable features: Define once, use in many models
Consistency: Same features in training and serving
Discovery: Find existing features before creating new
Lineage: Track feature origins and usage

# Benefits

Reduce duplicate feature engineering
Faster model development
Consistent training/serving features
Feature documentation and discovery

# Data Preparation Best Practices

# Data Types

Handle data types consistently:

Define schemas for input data
Validate types before processing
Handle missing values explicitly
Document type assumptions

# Code Reuse

Avoid duplicating logic:

Shared utility libraries
Common preprocessing functions
Centralized configuration
Package management

# Data Pipeline

Reproducible data preparation:

Version data transformations
Parameterize date ranges
Log data statistics
Test transformation logic

# Repeatability

Ensure experiments can be reproduced:

Seed random number generators
Version all dependencies
Document environment setup
Use containerization

# Data Testing

Validate data quality:

Input validation
Assertion checks
Statistical tests
Integration tests

# Lineage and Auditability

# Model Lineage

Track the complete model history:

Training data sources
Feature transformations
Model code version
Training parameters
Validation results

# Audit Requirements

Support regulatory and compliance needs:

Explain model decisions
Document model assumptions
Track model versions in production
Maintain prediction logs

# Governance Integration

# Data Access

Data science workflows respect platform governance:

Access only authorized datasets
Follow PII handling policies
Log data access for audit
Use approved data sources

# Catalog Integration

Document models in catalog
Link to training data
Describe intended use
Assign ownership

# Current Tools

Tool	Use Case	Notes
Jupyter Notebooks	Exploration	Local and cloud-based
Streamlit	Dashboards	Data Science and Credit teams
GitHub	Version control	Code and notebooks
Vertex AI	ML infrastructure	Future expansion

# Best Practices Summary

# Do

Version everything (code, data, models)
Document assumptions and decisions
Test data and model logic
Monitor production models
Collaborate through GitHub

# Don't

Deploy unversioned models
Skip validation steps
Ignore production monitoring
Create data silos
Duplicate feature logic

Data Catalog - Registering models and features
ETL - Data preparation pipelines
Reporting - Sharing DS insights