Who should use the Evaluate AI Models workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
2-step pipeline — follow each step in order
1Setup · Step 1 of 2
Open task pageTrain a model for evaluation
Use Kaggle to train a machine learning model on a chosen dataset, preparing it for performance evaluation. This step ensures the model is properly trained and ready for assessment.
Why this matters
Training creates the model that will be evaluated; without a properly trained model, evaluation cannot proceed.
What you get
A trained machine learning model is ready for evaluation.
More optionsCompare
2Deliver · Step 2 of 2
Open task pageEvaluate model performance
Use Together AI Platform to run evaluation metrics on the trained model, analyzing accuracy, precision, and other relevant measures to generate a comprehensive performance report.
Why this matters
Evaluation quantifies model quality and identifies areas for improvement, making it the core step of this workflow.
What you get
A detailed evaluation report with performance metrics is produced.
More optionsCompare