Playbooks Abacus AI
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Deploy a Real-Time Forecasting API with Abacus AI

Build, train, and launch a production‑ready forecasting model that serves predictions through a versioned API endpoint, with built‑in monitoring and automatic retraining.

Takes
Csv Database
Produces
Api Response
What you'll produce
Input
sales_data.csv with columns date, product_id, region, sales_amount
Output
API endpoint URL: api.abacus.ai/v1/forecast

The Workflow

8 steps · click a step number to mark it done

Open Abacus AI and have it ready before you start

Upload your historical CSV dataset or connect Abacus AI to your data warehouse (Snowflake, BigQuery, S3) via the Integrations tab

Select the target column to forecast and the time‑stamp column, then click Generate Models to let AutoML create candidate pipelines

Review the performance table, compare metrics such as MAE and RMSE, and choose the top‑performing model

Enable feature engineering options and turn on Automatic Retraining so new data is ingested daily

Deploy the selected model as a REST API endpoint, give it a version name, and click Deploy

Copy the generated endpoint URL and test it with a sample JSON payload using the built‑in Test Console

Configure monitoring alerts for data drift and prediction latency in the Monitoring dashboard

Integrate the endpoint URL into your application code to request real‑time forecasts

Ready to run this workflow?

Open Abacus AI and follow the steps above.

Open Abacus AI
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