Graphite Note Insights Lifecycle

No-code, Automated Machine Learning for Data Analytics Teams

Data to Insights Lifecycle

• Dataset: Begin with a dataset containing historical data.

• Feature Selection: Identify the most important variables (features) for the model.

• Best Algorithm Search: Test different algorithms to find the best fit for your data.

• Model Generation: Create a predictive model based on selected features and the best algorithm.

• Model Tuning: Fine-tune the model’s parameters to improve accuracy.

• Model Deployment: Deploy the final model for real-world usage.

• Explore Key Drivers: Analyze the key factors influencing the model’s predictions.

• Explore What-If Scenarios: Test different hypothetical situations to see their impact.

• Predict Future Outcomes: Use the model to forecast future trends or outcomes.