Data Privacy and Security

When collecting, storing and using training data on a large scale, the data needs to be encrypted and desensitized. At the same time, it is also necessary to comply with relevant laws and regulations to ensure the legal use and confidentiality of the data.

Data Management and Quality

Establish an effective data quality control mechanism, clean and preprocess the large amounts of collected data to eliminate errors, and ensure the quality and diversity of data to effectively train AI models and improve the model's generalization ability and coverage.

Data Bias and Imbalance

The data collected may be biased and imbalanced, causing model performance to degrade. During the data collection and preparation process, care needs to be taken to avoid bias and imbalance and ensure the representativeness and balance of the data.

Data Volume and Resource Requirements

Large-scale training data requires large amounts of storage and computing resources. As the amount of data increases, the training time and storage space of the model will also increase dramatically. This requires the use of efficient algorithms and distributed computing frameworks to accelerate the model training process, and optimize storage strategies to save space.