Help Center/ ModelArts/ ModelArts User Guide (Standard)/ Using ExeML for Zero-Code AI Development/ Introduction to ExeML
Updated on 2024-10-29 GMT+08:00
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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 sound classification, text classification, image classification, predictive analytics, and object detection. 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 finished 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. For more information about workflows, see What Is Workflow?.

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.

  • Object Detection

    An object detection project aims to identify the class and location of objects in images. You only need to add images and label objects in the images with proper bounding boxes. The labeled images will be used as a training set for building a model to identify multiple objects or provide the number of objects in a single image. Object detection can also be used to inspect employees' dress code and perform unattended inspection of article placement.

  • Predictive Analytics

    A predictive analytics project is an automated model training application for structured data, which can classify or predict structured data. Predictive analytics can be used for user profile analysis and targeted marketing, as well as predictive maintenance of manufacturing equipment based on real-time data to identify equipment faults.

  • Sound Classification

    A sound classification project identifies whether a certain sound is contained in an audio file. Sound classification can be used to monitor abnormal sounds in production or security scenarios.

  • Text Classification

    A text classification project identifies the class of a piece of text. It can be used in emotion analysis or news classification.