Explainable AI Made Simple
Keep doing what you do best – building amazing models. We'll handle the explainability. Transform black-box predictions into clear insights that drive better decisions and build trust in your AI.
Focus on Your Models, We'll Handle the Explanations
Just like Weights & Biases revolutionized experiment tracking for deep learning, CloudExplain makes explainable AI effortless for any machine learning workflow.
Train as Usual
Use your favorite ML libraries and frameworks. No changes to your existing workflow required.
Add few Lines
Wrap your model with CloudExplain and get instant explanations, feature importance, and bias detection.
Gain Insights
Discover what drives your model's decisions and improve performance with actionable insights.
Works with any ML framework - scikit-learn, TensorFlow, PyTorch, XGBoost, and more
Get consistent explanations across different models and frameworks. Compare interpretability between algorithms to choose the best approach for your use case.
Rich, interactive charts that make complex explanations easy to understand
From SHAP waterfall plots to feature importance rankings, visualize what matters most in your model's decisions with publication-ready charts.
Automatically detect and measure bias across different demographic groups
Ensure your models are fair and compliant with automated bias detection, fairness metrics, and recommendations for improvement.
Handle explanations for thousands of predictions with enterprise-grade infrastructure
Scale from single predictions to batch explanations of millions of samples. Our cloud infrastructure handles the computational complexity for you.
Share insights across teams with stakeholder-friendly reports and dashboards
Bridge the gap between data scientists and business stakeholders with clear, shareable explanations that everyone can understand.
Get started in minutes with our Python SDK and REST API
Add explainability to your existing ML pipeline with minimal code changes. Compatible with Jupyter notebooks, MLOps tools, and CI/CD workflows.
Get Started in few Lines of Code
It's really that simple. Here's how to add explainability to any model:
model=model,
X=X_simple,
y=y_test_simple,
model_version="0.13.0",
model_name="Adult model",
model_description="Model to predict if a person makes over 50k a year.",
explanation_name="Adult model v3 2025-06-14",
explanation_env="prod",
data_source="adult dataset",
ml_type="binary_classification",
is_higher_output_better=True,
feature_descriptions=feature_descriptions,
baseline_data=X_train,
api_token="your_api_token_here",
function_url="https://your-env-execute-containers.azurewebsites.net/api/upload_via_token"
) as run:
# Your integration code here
pass
Common Use Cases
Model Debugging & Improvement
Identify why your model makes certain predictions and discover opportunities for improvement
Regulatory Compliance
Meet GDPR, CCPA, and industry-specific requirements with transparent AI explanations
Stakeholder Communication
Build trust with business leaders and customers through clear, understandable explanations
Feature Engineering Insights
Discover which features matter most and guide your next iteration of feature engineering