The out-of-the-box and full-lifecycle AI development platform provides one-stop data processing, model development, training, management, and deployment.
Easy to Use
Various built-in open source models and automatic hyperparameter tuning help you start model training from scratch. Models can be deployed on the device, edge, and cloud with just one click.
The Huawei-developed MoXing framework delivers high-performance algorithm development and training. GPU utilization is optimized for online inference. Huawei Ascend chips significantly accelerate inference.
ModelArts supports multiple mainstream open source frameworks, such as TensorFlow and Apache Spark MLlib, mainstream GPUs, and the Huawei-developed Ascend AI chips. Exclusive use of resources and custom images ensure flexible development experience.
Modeling from Scratch
Developers with AI application requirements but no AI development capabilities. They are unfamiliar with AI development frameworks and cannot develop models themselves.
ModelArts provides ExeML to automate model design, parameter tuning and training, and model compression and deployment with the labeled data. The process is code-free and does not require experience with model development, allowing developers to start from scratch.
You can use ExeML to quickly create image classification, object detection, and predictive analytics models. New models are coming soon.
AI beginners with basic knowledge but limited AI development capabilities. They are able to use common AI development frameworks and open source tools to create simple models.
ModelArts offers built-in pre-trained algorithms. Without any coding, you can upload your own service data, select a desired built-in algorithm, retrain the algorithm to create a model, and deploy the model as a service.
ModelArts provides three types of algorithms: image classification, object detection, and image segmentation. You can create a training job based on the actual application requirements to obtain a desired model. More built-in models are being added.
AI engineers and experts with deep AI development capabilities, years of AI development experience, and extensive model development and optimization experience.
ModelArts integrates Jupyter Notebook. You can create a development environment, compile and debug the model training code, and use the compiled code to create a training job to train and deploy a model. ModelArts supports version management of datasets, training jobs, and models. It also provides traceback diagrams of datasets, training jobs, models, and services to visualize AI development workflows. This helps you easily manage AI development and improve AI development efficiency.
ModelArts allows you to customize and deploy deep learning and conventional machine learning models.
ModelArts manages data preparation, such as collection, filtering, and labeling, and dataset versions, especially for deep learning datasets.
Huawei's MoXing deep learning framework enables high-performance distributed training. To accelerate model development, it uses automatic hyperparameter tuning and standalone and distributed training.
ModelArts deploys models in various production environments such as devices, the edge, and the cloud, and supports online and batch inference jobs.
ModelArts supports code-free modeling and auto learning with image classification, object detection, and predictive analytics.
ModelArts works with Graph Engine Service (GES) to manage and visualize the lifecycle of AI development workflows, implementing data and model lineage.
ModelArts supports common models and datasets, and internal or public sharing of enterprise models in the marketplace.
Open beta test
Bare Metal Server (BMS) dedicated resource pool
Custom image training
Support for PyTorch v1.0.0
ExeML — Sound Classification
New data labeling
Labeling for sound classification, speech recognition, and named entity recognition
Multi-person labeling, model optimization, and video streaming services