* Refactor ML section - local and remote models Signed-off-by: Fanit Kolchina <[email protected]> * Added command to calculate checksum Signed-off-by: Fanit Kolchina <[email protected]> * Add ONNX format to register API Signed-off-by: Fanit Kolchina <[email protected]> * Add sparse encoding predict example Signed-off-by: Fanit Kolchina <[email protected]> * Add API section Signed-off-by: Fanit Kolchina <[email protected]> * Refactor the API section Signed-off-by: Fanit Kolchina <[email protected]> * Typo Signed-off-by: Fanit Kolchina <[email protected]> * Implemented Vale comments Signed-off-by: Fanit Kolchina <[email protected]> * Add get connector API Signed-off-by: Fanit Kolchina <[email protected]> * Reword heading Signed-off-by: Fanit Kolchina <[email protected]> * Addressed tech review comments Signed-off-by: Fanit Kolchina <[email protected]> * Apply suggestions from code review Co-authored-by: Melissa Vagi <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> --------- Signed-off-by: Fanit Kolchina <[email protected]> Signed-off-by: kolchfa-aws <[email protected]> Co-authored-by: Melissa Vagi <[email protected]>
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| layout | title | parent | has_children | nav_order |
|---|---|---|---|---|
| default | Train and Predict APIs | ML Commons API | true | 30 |
Train and Predict APIs
The ML Commons API lets you train machine learning (ML) algorithms synchronously and asynchronously, make predictions with that trained model, and train and predict with the same dataset.
To train tasks through the API, three inputs are required:
- Algorithm name: Must be one of a FunctionName. This determines what algorithm the ML Engine runs. To add a new function, see How To Add a New Function.
- Model hyperparameters: Adjust these parameters to improve model accuracy.
- Input data: The data that trains the ML model, or applies the ML models to predictions. You can input data in two ways, query against your index or use a data frame.
ML Commons supports the following Train and Predict APIs: