Forge AI Predictions UI

Documentation for the Prediction Dashboard in ForgeAI

Overview:

The Prediction Dashboard in ForgeAI, showcases how the platform generates predictions based on specific inputs using a machine learning model. The dashboard is designed to process real-time data and provide accurate predictions, allowing users to make informed decisions. The model applied here is linked to the current date and time, with relevant inputs fed into the system for analysis.

Key Sections:

1. Model Panel:
   • The dashboard displays the specific Model used for generating predictions. In this case, the model is timestamped: Wed Jul 31 2024 12:54:01 GMT-0600 (Mountain Daylight Time).
   • The timestamp can indicate when the model was last trained or updated, ensuring users are aware of the model's freshness and relevance.
2. Inputs Section:
   • This section provides two key input metrics used by the machine learning model to generate predictions:
3. Prediction Section:
   • The Prediction section displays the output from the machine learning model based on the provided inputs.
   • This section could change depending on the specific model in use, providing different predictions (such as churn probability, system health, or other business outcomes).

Model-Specific Insights:

• The prediction is tied directly to the inputs provided in the dashboard, showing that based on the current number of active users and dashboards, the system expects a high revenue outcome.
• In other contexts, this dashboard could be used to predict a range of different outcomes, depending on the model, such as customer churn, machine downtime, or sales forecasts.

Usage:

This Prediction Dashboard allows decision-makers to quickly assess real-time insights. By continuously monitoring inputs like user activity or dashboard interactions, users can predict outcomes such as revenue generation, equipment maintenance needs, or customer behavior.

Potential Actions:

• Revenue Maximization: Since the prediction shows a high revenue outcome, marketing or sales teams could take immediate action to amplify ongoing efforts during peak user activity periods.
• Model Monitoring: Ensure the machine learning model is updated regularly based on the most recent data for continued accuracy in predictions.
• Scalable Insights: Depending on the model type, use the dashboard to track and predict other critical KPIs such as production efficiency, customer retention, or supply chain optimization.

Conclusion:

This ForgeAI Prediction Dashboard provides an intuitive, real-time view into machine learning predictions. By leveraging dynamic inputs, the dashboard adapts to provide actionable insights, enabling users to make proactive, data-driven decisions based on accurate predictions.