Explore the comprehensive documentation for building, deploying, and managing intelligent applications with Azure's AI and Machine Learning services.
Azure provides a suite of services designed to empower developers and data scientists to build intelligent solutions. These services cover a wide range of AI capabilities, from machine learning model training and deployment to natural language processing, computer vision, and decision support.
A cloud-based environment for training, deploying, and managing machine learning models. It offers a comprehensive platform for the end-to-end machine learning lifecycle.
A collection of AI services that developers can use to add visual, speech, language, decision, and search capabilities to their applications.
A comprehensive, end-to-end cloud service for building, testing, deploying, and managing intelligent bots.
A fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. It's widely used for big data analytics and machine learning.
Explore practical code examples for various AI and Machine Learning scenarios:
# Example: Using Azure Machine Learning SDK for Python
from azureml.core import Workspace
try:
ws = Workspace.from_config()
print(f"Connected to workspace: {ws.name}")
except Exception as e:
print(f"Could not connect to workspace: {e}")
print("Please ensure you have a config.json file or specify connection details.")
Find more samples on GitHub.