Model Development

ModelArts Studio provides the model development function, covering all phases from model training to model calling. The platform supports full-process model lifecycle management, ensuring efficient and accurate execution of every phase from data preparation to model deployment, providing powerful intelligent support for real-world applications.

• Model training: ModelArts Studio provides abundant training tools and flexible configuration options for model training, which is the first step of model development. You can select an appropriate model architecture based on actual requirements and perform refined training using different training data. The platform supports distributed training and can process large-scale datasets, helping you quickly improve model performance. It supports various training types including pre-training, full-tuning, LoRA fine-tuning.
• Model evaluation: The platform provides comprehensive model evaluation functions to ensure the effectiveness of models in real-world applications. The automatic evaluation function allows you to continuously monitor key metrics such as model precision and recall rate during training to promptly identify and address potential issues. Model evaluation functions help you verify the accuracy and reliability of models in diverse application scenarios. It supports rule-based automatic evaluation, manual evaluation, and customization of evaluation metrics. Additionally, it allows you to score model performance from various evaluation metrics on the manual evaluation page.
• Model compression: Before model deployment, compression is a key step to improve inference performance. Through model compression, the memory occupied in the inference process can be effectively reduced, thereby saving resources and improving the computing speed.
• Model deployment: The platform provides a one-click deployment feature, allowing you to easily deploy trained models to either a cloud or on-premises environment. It supports multiple deployment options to meet the requirements of different scenarios. With flexible APIs, models can be seamlessly integrated into various applications.
• Model calling: After a model is deployed, you can use the model calling function to quickly access the model's services. The platform provides efficient APIs to help you easily integrate models into your applications and implement functions such as intelligent dialogue and text generation.