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
To ingest project data, TruEra supports the following methods:
In terms of task breakout, these comprise, at minimum:
- 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.
- 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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