Advanced Azure AutoML - Custom Machine Learning Models


What is Azure AutoML?

Azure AutoML is a service provided by Microsoft Azure that simplifies the process of building, training, and deploying custom machine learning models. It is designed to help data scientists and developers create powerful machine learning solutions with minimal manual effort, making it accessible to a broader audience.


Getting Started

To use Azure AutoML for custom machine learning models, follow these steps:

  1. Sign in to your Azure Portal.
  2. Create an Azure Machine Learning workspace.
  3. Prepare and upload your training data.
  4. Choose the type of machine learning model you want to build (classification, regression, etc.).

Sample Code

Below is an example of how to use Azure AutoML in Python:

import azureml.core
from azureml.core import Workspace, Experiment
from azureml.train.automl import AutoMLConfig
# Load your Azure Machine Learning workspace
ws = Workspace.from_config()
# Set up an experiment
experiment = Experiment(ws, 'my-experiment')
# Define AutoML configuration
automl_config = AutoMLConfig(task='classification',
                             primary_metric='AUC_weighted',
                             training_data='training_data.csv',
                             label_column_name='target',
                             n_cross_validations=5,
                             iteration_timeout_minutes=10,
                             max_concurrent_iterations=4)
# Submit the experiment
run = experiment.submit(automl_config)
run.wait_for_completion(show_output=True)

Conclusion

Azure AutoML simplifies the process of creating custom machine learning models, allowing you to harness the power of AI without being an expert in machine learning. It is a valuable tool for a wide range of applications, from predictive analytics to recommendation systems.