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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
NetSuite saved searches contain valuable business logic and filtering criteria, but converting them into machine learning training datasets usually means manual CSV exports with inconsistent formatting. This creates data quality issues that can compromise model performance.
Here’s how to transform your existing saved searches into reliable, automated ML training datasets without losing the search logic you’ve already built.
Preserve search logic while automating ML dataset creation
Coefficient maintains your existing NetSuite saved search criteria while providing automated data extraction for ML workflows. Unlike manual exports that require constant intervention, the Saved Searches import method preserves your search logic and delivers consistent formatting.
The real advantage is automated refresh scheduling that keeps training datasets current without manual intervention. Your ML models get fresh data while maintaining the business rules embedded in your saved searches.
How to make it work
Step 1. Import existing saved searches directly.
Select any saved search from your NetSuite account. The import preserves all search criteria, filters, and calculated fields you’ve already configured, eliminating the need to rebuild complex search logic.
Step 2. Configure automated refresh scheduling.
Set up daily, weekly, or hourly refreshes to ensure your ML training datasets stay current. The system handles search execution automatically and provides error handling for failed searches.
Step 3. Optimize data structure for ML frameworks.
Use drag-and-drop column reordering to arrange fields in the sequence your ML framework expects. The real-time preview shows the first 50 rows so you can validate data structure before full import.
Step 4. Combine multiple searches for comprehensive datasets.
Import multiple saved searches to create comprehensive training datasets. Use the spreadsheet environment for feature engineering, data cleaning, and format standardization before feeding into Python ML frameworks.
Turn business logic into ML-ready datasets
Your NetSuite saved searches already contain valuable business intelligence. Converting them into automated ML training datasets preserves that logic while eliminating manual export headaches. Start building your automated ML datasets today.
