Types of AI Workloads
Lesson 1.2: Types of AI Workloads
In this lesson, you'll learn about the different types of AI workloads and how they are categorized based on the tasks they perform.
📦 Common AI Workloads
- Machine Learning: Training models on historical data to make predictions or classifications on new data.
- Anomaly Detection: Identifying unusual patterns that do not conform to expected behavior, often used in fraud detection or equipment monitoring.
- Computer Vision: Processing and analyzing visual data (images or video), such as recognizing objects or detecting faces.
- Natural Language Processing (NLP): Interpreting and generating human language, including speech and text.
- Conversational AI: Building bots and virtual assistants that understand and respond to user input in natural language.
🔁 Training vs Inference
- Training: The process of teaching an AI model using historical data and labels to adjust internal parameters.
- Inference: Using a trained model to make predictions or decisions based on new input data.
🏢 Business Use Cases
- Retail: Product recommendation systems using machine learning.
- Healthcare: Computer vision for analyzing X-rays and scans.
- Customer Service: Chatbots and sentiment analysis using NLP.
- Finance: Anomaly detection for fraudulent transactions.
🧠 Summary
- AI workloads are grouped into categories like machine learning, vision, language, and conversational AI.
- Training builds models using data, while inference uses them to generate output.
- Understanding AI workloads helps in selecting the right tools and services on Azure.
✅ Next Lesson
Lesson 1.3: Introduction to Azure AI Services
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