Building an End-to-End AI Workflow for Predictive Analytics

Objective: Learn how to create a predictive analytics pipeline, integrating pre-built AI modules and custom configurations.

1. Setup Your Workspace

   • Import a sample dataset (e.g., sales or logistics data) using the Data Input square.

   • Configure preprocessing squares for data cleaning, normalization, and handling missing values.

2. Integrate a Pre-Trained Model

   • Drag and drop a Pre-Trained Regression Model square from the library.

   • Configure model parameters, including prediction targets and training/test data splits.

3. Define Output and Deployment

   • Add a Visualization Square to generate insights (e.g., trends, forecasts).

   • Deploy the workflow using the integrated decentralized GPU architecture for optimized scalability.

4. Test and Iterate

   • Use the Workspace's built-in validation tools to test predictions against actual data.

   • Iterate on model parameters or preprocessing steps to improve accuracy.