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.
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.
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.
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.
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.
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