Basic Process of Large Model Development

Large models are characterized by their numerous parameters and complex architectures. The process of developing a large model consists of the following steps:

• Dataset preparation: The performance of a large model depends on a large amount of training data. Therefore, dataset preparation is the first step of model development. First, collect raw data based on service requirements to ensure data coverage and diversity. For example, an NLP task may require a large amount of text data, and a CV task requires image or video data.
• Data preprocessing: Data preprocessing is an important part of data preparation to improve data quality and adapt to model requirements. Common data preprocessing operations are as follows:
  • Deduplication: Ensure that each data record in the dataset is unique.
  • Filling missing values: Fill missing parts in data. Common methods include mean filling, median filling, and missing data deletion.
  • Data standardization: Convert data into a unified format or range, especially when processing numeric data (such as normalization or standardization).
  • Denoising: Remove irrelevant or abnormal values to reduce interference to model training.

  The purpose of data preprocessing is to ensure the quality of datasets, enabling effective model training and reducing negative impacts on model performance.

• Model development: Model development is the core phase of a large model project and usually includes the following steps:
  • Model selection: Select a proper model based on the task objective.
  • Model training: Use the processed dataset to train a model.
  • Hyperparameter tuning: Select proper hyperparameters such as the learning rate and batch size to ensure that the model can quickly converge and achieve good performance during training.

  The key to the development phase is to balance the model complexity and compute resources, avoid overfitting, and ensure that the model can provide accurate predictions in actual applications.

• Application and deployment: After a large model is trained and verified, it enters the application phase, which includes the following:
  • Model optimization and deployment: Trained large models are deployed in production environments and inference services can be provided via cloud services or local servers. In this case, the response time and concurrency capabilities of the model must be considered.
  • Model monitoring and iteration: Continuously monitor the performance of a deployed model, and periodically update or retrain the model based on feedback. As new data is introduced, the model may require adjustments to maintain stable performance in real-world applications.

  In the application phase, the model can be integrated into a specific service process and also needs to be continuously optimized based on service requirements to make the model more accurate and efficient.