Comprehensive evaluation framework for Fortune 500 company reduced model selection time by 70% while improving accuracy by 15% through systematic benchmarking and bias detection
A Fortune 500 financial services company was struggling with model selection for their credit risk assessment system. Their data science team was spending months manually evaluating different machine learning models, comparing performance metrics across various datasets, and attempting to identify potential biases in their predictions. This lengthy process was creating bottlenecks in their AI/ML deployment pipeline and delaying critical business decisions.
The key challenges included:
We developed an automated Model Evaluation Suite that standardized and accelerated the entire model evaluation process. The system integrated multiple evaluation frameworks and provided comprehensive bias detection capabilities.
Built a scalable evaluation pipeline using MLflow and Weights & Biases to automatically test models against standardized datasets and metrics.
Implemented comprehensive fairness metrics using IBM's AIF360 toolkit to detect and quantify potential biases across protected attributes.
Created interactive dashboards using Plotly and Streamlit to visualize model performance, bias metrics, and comparative analysis.
Developed automated report generation that produces detailed evaluation summaries, including recommendations for model selection.
# Core evaluation pipeline
class ModelEvaluationSuite:
def __init__(self, config):
self.config = config
self.mlflow_client = MlflowClient()
self.wandb_run = wandb.init(project="model-evaluation")
def evaluate_model(self, model, X_test, y_test, protected_attrs=None):
# Performance metrics
performance_metrics = self.calculate_performance_metrics(model, X_test, y_test)
# Bias detection
bias_metrics = self.detect_bias(model, X_test, y_test, protected_attrs)
# Interpretability analysis
interpretability_scores = self.analyze_interpretability(model, X_test)
# Log to MLflow and W&B
self.log_metrics(performance_metrics, bias_metrics, interpretability_scores)
return {
'performance': performance_metrics,
'bias': bias_metrics,
'interpretability': interpretability_scores
}
def detect_bias(self, model, X_test, y_test, protected_attrs):
from aif360.datasets import StandardDataset
from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric
# Convert to AIF360 format
dataset = StandardDataset(
df=pd.concat([X_test, y_test], axis=1),
label_name=y_test.name,
protected_attribute_names=protected_attrs
)
# Calculate bias metrics
predictions = model.predict(X_test)
classified_dataset = dataset.copy()
classified_dataset.labels = predictions.reshape(-1, 1)
metric = ClassificationMetric(dataset, classified_dataset,
unprivileged_groups=[{protected_attrs[0]: 0}],
privileged_groups=[{protected_attrs[0]: 1}])
return {
'equalized_odds_difference': metric.equalized_odds_difference(),
'average_odds_difference': metric.average_odds_difference(),
'disparate_impact': metric.disparate_impact(),
'statistical_parity_difference': metric.statistical_parity_difference()
}# Streamlit dashboard for model comparison
import streamlit as st
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def create_model_comparison_dashboard():
st.title("Model Evaluation Dashboard")
# Model selection
models = load_evaluated_models()
selected_models = st.multiselect("Select models to compare", models)
if selected_models:
# Performance comparison
fig = create_performance_comparison_chart(selected_models)
st.plotly_chart(fig)
# Bias metrics comparison
bias_fig = create_bias_comparison_chart(selected_models)
st.plotly_chart(bias_fig)
# Feature importance comparison
importance_fig = create_feature_importance_chart(selected_models)
st.plotly_chart(importance_fig)
# Generate recommendations
recommendations = generate_model_recommendations(selected_models)
st.subheader("Recommendations")
st.write(recommendations)