AzureML Studio : Notebooks

In this notebook, we will use the AzureML Studio's Notebooks to design, train, test and deploy our data processing pipeline.

The experiment is visible in the AzureML Studio : oc-p7-notebook

Notebook code

The code deployed in the Notebooks environment consists of :

• main.ipynb : this is the main Notebook where our data is prepared, our model is built, trained, deployed and tested
  • Prepare : prepare the data for our model
  • Train : we use the best model from 8_keras_neural_networks.ipynb : Stacked Bidirectional-LSTM layers on Embedded text
  • Deploy : we deploy the model in an ACI (Azure Compute Instance), which will expose a REST API to query our model for inference
  • Test : we run a POST query to check that our model works
• score.py : this is the code deployed in the ACI for inference
  • init() : load the registered model
  • run(raw_data) : process data sent to the REST API and predict the sentiment with the loaded model
• conda_dependencies.yml : this defines the dependencies that must be installed in the Inference environment

Model training

We can observe the model performances while training :

Query result

We can observe the result of a query to the REST API :