AI Model and Dataset Management

AI Model Management

Cloud-Based Model Training and Deployment

AI model training within Alliance Games occurs primarily in AG Game Cloud, where large-scale resources are available for processing complex datasets. This setup allows models to be continuously refined and updated without impacting in-game performance. By managing model training in the cloud, Alliance Games can make iterative improvements to AI models, enabling them to learn from new player interactions and adapt to evolving gameplay scenarios.

• Centralized Training, Decentralized Deployment: Models are trained centrally in the cloud but deployed across the decentralized network to ensure quick and reliable access for in-game AI systems.

• Scalable Model Architecture: The cloud architecture enables the training and deployment of multiple versions of AI models, allowing Alliance Games to test new features or improvements and roll out updates in stages.

• Model Version Control: A robust version control system tracks model iterations, making it possible to revert to previous versions if needed and to maintain an archive of model development for benchmarking and testing.

Continuous Model Update Mechanisms

To ensure AI models remain current, Alliance Games uses continuous update mechanisms to synchronize new versions of models across the network.

• Incremental Model Updates: Instead of replacing entire models, updates often include only incremental changes, such as newly learned behaviors or specific algorithm adjustments. This minimizes resource use and reduces downtime.

• Distributed Model Syncing: NetFlow manages the distribution of model updates, pushing changes to Edge and Micro Nodes efficiently and ensuring that all parts of the network operate on the same model version.

• Scheduled Downtime for Major Updates: For significant model changes, scheduled downtime allows the network to transition smoothly, maintaining system stability.

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Dataset Management

Data Collection and Aggregation

Datasets are continuously gathered from in-game interactions, providing the raw data needed to refine AI models over time. DataVault serves as the primary repository for this data, offering a decentralized, secure storage solution.

• Real-Time Data Capture: Edge and Micro Nodes capture player interactions and in-game events in real-time, ensuring datasets remain up-to-date and reflect current gameplay trends.

• Data Aggregation and Filtering: NetFlow aggregates data from multiple sources, filtering redundant or irrelevant information and creating clean datasets for training.

• Anonymized Player Data: To ensure player privacy, DataVault stores player information in an anonymized form, adhering to data protection standards and enabling compliance with privacy regulations.

Dataset Processing and Preprocessing

Before data can be used for model training, it is processed and prepared to meet the requirements of each AI model. This preprocessing stage optimizes datasets, ensuring AI models learn efficiently from high-quality data.

• Data Normalization: Standardizes data formats and scales values to prevent model bias, improving the accuracy and stability of training sessions.

• Feature Engineering: Extracts essential features from raw data, creating variables that improve model learning while minimizing unnecessary data points.

• Batch Processing: Large data batches are processed in the cloud, where resources are available for intensive data tasks. These batches are then segmented and sent to local nodes for further refinement or model training as needed.

Dataset Version Control

To maintain data integrity and allow for ongoing model improvement, Alliance Games tracks all changes made to its datasets, using version control to manage historical data and prevent data loss.

• Data History and Rollback: Each dataset modification is logged, allowing developers to track changes and roll back to previous dataset versions if necessary.

• Benchmarking Data Versions: Different data versions are tested against model benchmarks, ensuring that new data does not introduce errors or reduce model performance.

• Selective Data Retention: Outdated or less relevant data is archived to reduce storage load while maintaining an accessible record for long-term analysis.