AI Development Lifecycle

Simple, powerful, and accurate AI development

Core Functions of AI Development

Data Management

Up to 80% reduction in manual data processing costs

ModelArts includes nine labeling tools to manage four types of datasets including text, images, audio, and video. ModelArts also offers auto labeling and team labeling, for more efficient dataset labeling than ever. ModelArts provides data processing capabilities, such as data cleansing, enhancement, and verification, backed up by flexible, visualized management of dataset versions. Import and export your data sets with ease as you develop and train your models.

Development Management

Access cloud-based services via your local IDE

ModelArts allows you to develop algorithms directly on the console or by calling Python SDKs. User-friendly SDKs let you access ModelArts via your local IDE to create and train models and deploy models as services.

Training Management

Train high-precision models faster

Powered by an EI-Backbone, ModelArts workflow excels at:

-   Greatly reducing the cost of data labeling by training high-precision models using small volumes of data.

-   Quickly improving the model precision by using the full-space
network architecture search and automated hyper-parameter tuning.

-   Significantly reducing training costs by employing pre-trained models to shorten the time required for deploying a trained model from weeks to minutes. All of these promote inclusive AI.

Model Management

Manage all iterated and debugged models in a unified manner

AI model development and tuning require frequent iterations and debugging. Changes in datasets, training code, or parameters may affect the quality of models. If the metadata of the development process cannot be managed in a unified manner, the best model may fail to be reproduced. ModelArts allows you to import models generated with all training versions from training jobs, templates, container images, and OBS.

Deployment Management

One-click deployment of models to the device, edge, and cloud

ModelArts models can be deployed as real-time, batch, or edge services. Real-time services process a large volume of highly-concurrent data. Batch services feature high-throughput capability of quickly processing data. Edge services feature the capability of completing inference locally in a highly flexible way.

Image Management

Custom image function allows users to customize engines

ModelArts uses container technology at the bottom layer so you can create container images and run them on ModelArts. The custom image function supports command line parameters and environment variables in free-text format. The custom images are highly flexible and support the job boot requirements of any computing engine. 

Application Scenarios

Product Inspection
Traditional manual quality inspection is labor- and time-intensive and defects are often missed. ModelArts detects and classifies defects based on device parameters and production images, which means more efficiency and lower costs.
ModelArts in Industries
Taking advantage of existing industry data, ModelArts trains models to predict potential issues and assist with quick decision making through knowledge inference. ModelArts integrates industry expertise with AI.

Featured Stories

Rainforest Connection

Rainforest Connection (RFCx) is a non-profit organization dedicated to protecting the world's rainforests. RFCx used HUAWEI CLOUD AI services and ModelArts to build an intelligent model capable of detecting and decoding the spider monkey sounds, to learn about their habitat, of threats to their survival, and about their daily behaviors. This information helps the forest rangers keep the spider monkeys safe.

Center for Excellence in Brain Science and Intelligence Technology, the Chinese Academy of Sciences

ModelArts can automatically trace and reconstruct neurons with an accuracy and recall rate of up to 95%. Using parallel computing enabled by the ultra-large clusters of ModelArts, the total time required for morphological reconstruction of 100,000 neurons can be reduced from 125 person-years to just 10 days, and the cost of reconstructing a single neuron can be reduced to 1/77 the original cost. If the study were to be carried out on mice or macaques, the cost reduction and efficiency gains would be even more significant.

Register with HUAWEI CLOUD to get free services

Register Now