🪔
🎉 Festival Dhamaka Sale – Upto 80% Off on All Courses 🎊
🎁
INDIA'S NO. 1 INTERNSHIP PORTAL
🎉 Festival Dhamaka Sale – Upto 80% Off on All Courses 🎊
🎁INDIA'S NO. 1 INTERNSHIP PORTAL
Drag-and-drop modules to build ML pipelines without writing code.
Automatically selects algorithms and tunes hyperparameters for optimal performance.
Run Python, R, or custom scripts within your ML workflows.
Track experiments, manage models, and deploy with CI/CD pipelines.
Set up a workspace in Azure Portal to manage datasets, models, and compute resources.
Access the visual designer or notebook interface to start building ML workflows.
Use drag-and-drop modules or write custom code to train models on cloud compute.
Use built-in metrics, AutoML, and hyperparameter tuning to improve performance.
Deploy models as REST endpoints and monitor with Azure Monitor and Application Insights.
from azureml.core import Workspace, Experiment
from azureml.train.automl import AutoMLConfig
from azureml.core.dataset import Dataset
# Connect to workspace
ws = Workspace.from_config()
# Load dataset
dataset = Dataset.get_by_name(ws, name='your-dataset')
# Configure AutoML
automl_config = AutoMLConfig(
task='classification',
training_data=dataset,
label_column_name='target',
iterations=10,
primary_metric='accuracy',
compute_target='cpu-cluster'
)
# Run experiment
experiment = Experiment(ws, 'automl-classification')
run = experiment.submit(automl_config)
run.wait_for_completion()Quickly build and test ML models using visual workflows and AutoML.
Deploy models securely with Azure Kubernetes Service and REST endpoints.
Predict customer behavior, optimize campaigns, and personalize experiences.
Build models for credit scoring, fraud detection, and forecasting.
Explore Azure ML Studio’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Azure ML Studio’s capabilities, usage, and ecosystem.