# Data Ingestion¶

Ingesting data means importing large data files from multiple sources into a single, cloud-based storage medium — a data warehouse, data mart, or database — from which it can be accessed and analyzed by TruEra.

First things first

Remember to first create a project before attempting to ingest data. Review the Quickstart for an brief overview of the ingestion process using sample data.

To ingest project data, TruEra supports the following methods:

In terms of task breakout, these comprise, at minimum:

*Pre-deployment***Feature Development**– transforming raw data into features that better represent the underlying problem, resulting in improved model accuracy on unseen data.**Model Training**– fitting the best combination of weights and bias to minimize loss functions over the prediction range.*Post-deployment***Logging Inputs and Predictions**– classifying whether a particular log event, or set of events, is causing a real incident that requires attention.**Logging Additional Metrics**– score tracking to determine real accuracy and improvement —F1,Measures a model's accuracy by combining its precision and recall scores; computes how many times a model made a correct prediction across the entire dataset.F2,Weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precisionbrier_loss; iteration-level metrics (learning curves); predictions after every epoch; and updated experiment metrics among many others.Measures the mean squared difference between the predicted probability and the actual outcome.

Above all, have a plan for tracking and handling your model's results, both expected results and unexpected, so you can refine and improve your data and model all along your path to ultimate success.

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