Model Training

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Model Training


ML model training matches an ML algorithm with selected featureset training data in order to learn. The process of learning involves finding patterns in the training data that map the input data attributes to the target, or, the value that you want to predict.  A successfully trained ML model captures these patterns. Once it does, it can be deployed to perform predictions.

To start your model training:

  1. Select “Training” under Machine Learning

  2. You will be directed to a list of all your completed and ongoing training jobs

  3. On the top right corner, select “Train a New Model”

  4. Fill in the name and description of your ML model

  5. Select the appropriate featureset from the drop-down menu

Configure your model according to your requirements.

ParameterComment
model_definition_id The architecture id of the model which is dependent upon the kind of ML problem you are trying to solve. Allowed values are image_classification_sc_cnn, image_classification_sc_cnn_resnet, text_extraction_lstm_crf_tfhub, text_classification_sc_usel

auto_batch_size
The number of training examples utilized in one iteration.

no_of_epochs
An epoch is a full iteration over the training data.

learning_rate
Learning rate controls how quickly or slowly a model learns a problem. It is a very small number, usually something like 0.001, that we multiply the gradients by to scale them.
layers.size A layer is a container that usually receives weighted input, transforms it with a set of functions and then passes these values as output to the next layer.
layers.activation_function An activation function takes in weighted data (matrix multiplication between input data and weights) and outputs a non-linear transformation of the data.
optimizer Optimizers shape and mould your model into its most accurate possible form by futzing with the weights.

loss_function
It’s a method of evaluating how well your algorithm models your dataset. A smaller value generally indicates a successful model which understands your data well.
metrics Evaluating criteria to define your model’s success like accuracy, loss etc.

What’s Next?