Introduction to ExeML

ExeML Functions

ModelArts ExeML is a customized code-free model development tool that helps you start codeless AI application development with high flexibility. ExeML automates model design, parameter tuning and training, and model compression and deployment based on the labeled data. With ExeML, you only need to upload data and perform simple operations as prompted on the ExeML GUI to train and deploy models.

You can use ExeML to quickly build models for image classification, object detection, predictive analytics, sound classification, and text classification. ExeML is widely used in industrial, retail, and security sectors.

• Image classification: identifies a class of objects in images.
• Object detection: identifies the position and class of each object in an image.
• Predictive analytics: classifies or predicts structured data.
• Sound classification: classifies and identifies different sounds.
• Text classification: identifies the category of a piece of text.

ExeML Process

With ModelArts ExeML, you can develop AI models without coding. You only need to upload data, create a project, label the data, train a model, and deploy the trained model. For details, see Figure 1. In the new-version ExeML, this process can be conducted by a workflow. You can develop a DAG through a workflow. DAG execution is to use a task execution template to perform data labeling, dataset publishing, model training, model registration, and service deployment in sequence.

Figure 1 ExeML process

ExeML Projects

• Image Classification

  An image classification project aims to classify images. You only need to add images and label them. Then, an image classification model can be quickly generated for automatically classifying offerings, vehicle types, and defective goods. For example, in the quality check scenario, you can upload a product image, label the image as qualified or unqualified, and train and deploy a model to inspect product quality.