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Integrate data import, data processing, model development, model training, model evaluation and service online to provide a one-stop comprehensive deep learning modeling process to quickly build AI services.
Efficiently manage and allocate hardware resources, schedule model training and model services to bring a speedy experience.
Simple operation and quick to start with minimal requirement of user operation, maintenance, software and hardware configuration.
Support multiple deep learning and machine learning frameworks such as TensorFlow, PyTorch, Caffe and MXNet.
Integrate Hadoop big data ecosystem; provide Jupyter Notebook online interactive development environment; support data and resource sharing and team collaboration to further improve model development efficiency.
Support multiple frames of AI model training including TensorFlow, MXNet, Caffe, and PyTorch; support distributed and GPU acceleration and provide TensorBoard visual model training process.
Support one-stop deployment management from model development to production environment, provide unified RESTful and gRPC model service APIs; manage model version and grayscale publishing; monitor service traffic in real time and enable elastic scaling based on resource utilization.
Support object-oriented storage, online data cleaning, online data labelling system, raw data pre-processing with one-click to import into platform storage.
Support GPU accelerated training, support distributed training, and visually deploy training jobs. Use priority queues to create batch training tasks.
Given agile model development, training results are imported into the model warehouse thus can be released and deployed. Support online model version management, model CI pipelines, model service grayscale release and elastic scaling.
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