AI Studio
  • AI Studio Guide
  • AI Studio Basics
    • About AI Studio
      • AI Studio Feature Guide
      • Problem Statements
      • Platform Use Cases
    • Key Terminology to Know
  • Building Your First Project
    • Image Tagging
  • Detailed Guide: Image Tagging
    • Your Data
      • Supported Formats & Image Specifications
      • Exploring Datasets
        • Creating New Datasets
        • Adding Data
        • Removing Data
      • Pre-Processing Results
    • Creating and Training Models
      • Training Basics
    • Evaluating Models
      • Default Evaluation Dataset
      • Interpreting Evaluation
        • Interpreting results
      • Improving Your Model
    • Inference
    • Deployment
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  1. Detailed Guide: Image Tagging

Creating and Training Models

AI Studio takes a data-centric, iterative approach to building production-ready fashion AI models.

The DIY AutoML platform of AI Studio empowers users to build self-learning AI models that deliver high levels of accuracy. Without writing a single line of code, the user creates and trains models using their own data. They simply need to create a project, add labels, choose categories, upload datasets and start training the model. Taking advantage of the pre-processing results and actionable insights, they can iterate the model until they achieve the highest possible levels of accuracy.

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Last updated 3 years ago

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