Model Deployment

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Model Deployment

Deploying machine learning models is the process of putting models into production and sending them to other business systems. Model deployment allows other systems to send in data to receive predictions.  These predictions are then returned to the company systems. Through deployment, you and your business can take full advantage of the model you built. Successful model training delivers a hosted model that is used for predictions.

To deploy your model:

  1. Select “Model Deployment” under Machine Learning. Your list of successfully trained models will appear

  2. Click on ‘Deploy’ button under ‘Deployment’ tab

Configure your model according to your requirements.

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

The number of training examples utilized in one iteration.

An epoch is a full iteration over the training data.

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.

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.

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