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If you don’t have your own model, you can create a new one through Auto ML. .

Whenever a user creates a new project, a default AutoML model, ‘XGBoost_default,  gets trained for default prediction and default explainability. In case a user wants to use his own model for explainability, the same can be done through:

  1. Uploading own model OR
  2. Training the model, for which inbuilt modelling techniques - Tree-based, probabilistic and linear, are present in AryaXAI, namely:

        - XGBoost

       - LGBoost

        - CatBoost

        - RandomForest

        - SGD

        - Logistic Regression

        - GaussianNaiveBayes

You can fine tune these models and tune the hyper parameters.

AryaXAI - AutoML

Through GUI

To upload a model:

Note: You can only upload a new model within an existing project where the initial data iteration has been uploaded.

Once the above criteria are met:

  1. Access the 'Settings' section and navigate to the 'Model Upload' tab
  2. Complete the necessary fields, including:

          - Model Name

          - Model architecture (supporting Machine Learning and Deep Learning)

          - Model Data tags (representing the data used to train the model)

          - Model file upload (.pkl and .h file types are supported)

Once the above steps are executed, the newly uploaded model becomes visible in the AutoML section.

To activate the new model for use, you must manually select it under 'Options' within the 'Model Versions' tab.

Model Performance

The default performance of the model trained is tracked under the ‘Model Performance’ tab. 

Train Model

To train a model:

- Upon selecting a model type, you can configure the Data Configuration, which mirrors the settings used during the initial data upload.

- Choose 'Save initial configuration' and 'Save Feature Encoding' for consistency and accuracy in the model training process.

- Customize the model parameters to tailor the training process according to specific requirements.

- After setting up the data configuration and model parameters, selecting 'Update' initiates the model training process.

Once the model is successfully trained, a comprehensive list of all versions is accessible and listed in the 'Model Versions' tab.

Note: A maximum of 10 models can be trained within a workspace. Within a project, only 2 models can be trained. (Considering workspace limitations, a maximum of 5 projects can be created.)

You need to activate the new model manually under ‘Options’ in the ‘Model Versions’ tab. 

Upon activating a model, detailed information becomes available within the 'Model Info' section, providing a comprehensive overview of the model, which includes:

- Model name

- Model Type

- Model Params

- Data tags

- Modelling info, which shows the details used for training the model


For the (activated) model you have trained, if you want to derive inferences for any tag, AryaXAI offers the ‘Inferencing’ section. In this section, you can run predictions using the activated model on specific files or tags.

Post-inference execution, the results are stored as tags, which are listed in the 'Inferencing Files' section. The list displays essential details such as the model name, the creation date of the inference, and performance metrics like accuracy, recall, and precision, among others. You also have the option to download this data if needed. 

The same tags generated from the inference process are accessible within the ML Monitoring section, providing a unified view of the inferences made by the model.

Through SDK

Additionally, you can also use the AryaXAI python package for tasks like training models, activating models for a project, model inferencing, or case info on projects with just a few lines of code.

Upload a Model:


Help function to upload a model:


To check all the models currently active or staged in your project via SDK, you can use the below command. 'Status' column will give the current status info.

# train model

# all trained models

The below function performs predictions on testing data using the XGBoost default model. You have the flexibility to pass any model you prefer, or leave it blank to use the default model.

testresults = project.model_inference(tag="Testing",model_name="XGBoost_v1")

Additional Functions:

# Train model. This Trains Another version Model with Current config (If config not passed)

# get Active model details
modelinfo = project.model_summary()

# available models to train 

# set model active for project

# get current data config details. If you don't modify the data settings, any future fine-tuning will use the same data settings

# remove model

# model inference
# model_name optional default to active model for the project

#The inferencing results are stored as 'Testing_XGBoost_v1_Inference' tag.

# project Cases 
project.cases()  # #last 20 case list

# project Case Info 

To get the Help functions:

# Help on method model_inference

# Help on method train_model

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