Azure Machine Learning Models

Overview

Explore the diverse range of models available within Azure Machine Learning. From pre-trained models for common tasks to guidelines for building and deploying your own custom models, this section provides comprehensive documentation to help you leverage the power of AI.

Featured Models

Image Classification

Classify images into predefined categories. Ideal for content moderation, product categorization, and visual search.

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Object Detection

Identify and locate objects within an image. Useful for autonomous driving, surveillance, and manufacturing quality control.

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Text Classification

Categorize text into predefined labels. Applications include sentiment analysis, spam detection, and topic modeling.

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Natural Language Processing

Understand and process human language. Includes capabilities like translation, summarization, and question answering.

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Forecasting Models

Predict future values based on historical data. Essential for demand planning, financial forecasting, and resource management.

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Custom Model Deployment

Deploy your own custom-trained machine learning models to Azure for scalable inference.

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Detailed Documentation

Model Categories

Image Classification

Azure Machine Learning offers robust solutions for image classification. You can utilize pre-trained models or train custom classifiers. This section covers:

  • Supported pre-trained models (e.g., ResNet, VGG, MobileNet).
  • Data preparation and augmentation techniques.
  • Training and evaluating image classification models.
  • Deployment options for real-time or batch inference.

Example: Using a Pre-trained Model

Here's a Python snippet demonstrating how to load a pre-trained image classification model:


from azureml.core import Workspace
from azureml.core.model import Model

ws = Workspace.from_config()

# Load a pre-trained image classification model
model_name = "resnet50"
model = Model(workspace=ws, name=model_name, version="latest")

print(f"Model Name: {model.name}")
print(f"Model Version: {model.version}")

Object Detection

Detect multiple objects within an image and draw bounding boxes around them. Key aspects include:

  • Leveraging models like YOLO and Faster R-CNN.
  • Annotation tools and best practices.
  • Training strategies for accurate detection.
  • Integrating object detection into applications.

Text Classification

Categorize text data with high precision. This involves:

  • Using BERT, RoBERTa, and other transformer-based models.
  • Text preprocessing pipelines.
  • Fine-tuning models for specific domains.
  • Common use cases like sentiment analysis and topic labeling.

Natural Language Processing (NLP)

A broad category encompassing tasks like:

  • Machine Translation: Translate text between languages.
  • Text Summarization: Generate concise summaries of longer texts.
  • Question Answering: Build systems that can answer questions based on provided context.
  • Named Entity Recognition (NER): Identify and classify named entities.

Forecasting Models

Predict future trends with Azure ML's forecasting capabilities.

  • Time series analysis and modeling.
  • Algorithms like ARIMA, Prophet, and LSTM networks.
  • Feature engineering for time series data.
  • Evaluating forecasting accuracy.

Custom Model Deployment

Deploy any machine learning model trained outside Azure ML or with custom frameworks:

  • Packaging models with their dependencies.
  • Creating Docker containers for deployment.
  • Deploying to Azure Container Instances (ACI) or Azure Kubernetes Service (AKS).
  • Setting up real-time endpoints and batch inference jobs.

Steps to Deploy a Custom Model

  1. Register your model in Azure ML.
  2. Define an inference script (score.py).
  3. Define the environment your model needs.
  4. Create an Azure ML entry script and environment.
  5. Deploy as a web service.

Model Management and Governance

Learn how to effectively manage your ML models throughout their lifecycle:

  • Model registry and versioning.
  • Model lineage tracking.
  • Model testing and validation.
  • Monitoring deployed models for performance drift.

Responsible AI

Build trustworthy and ethical AI systems.

  • Fairness assessment and mitigation.
  • Explainability techniques (e.g., SHAP, LIME).
  • Privacy-preserving ML.
  • Robustness and security considerations.