Documentation - Production Deployment & Consulting

For studios that require additional assurance when deploying machine learning systems in live projects, I provide optional consulting support for DataPredict-based integrations.

This is intended for teams that:

• Want to minimize risk when introducing adaptive systems.

• Require validation of model choice and deployment strategy.

• Need help interpreting live performance metrics.

• Need help selecting appropriate features for models’ inputs.

• Are deploying ML for revenue, retention, or game optimizations.

Typical Deployment Approach

A typical production deployment follows these principles:

• Baseline Pretraining: Models are initialized using designer-provided or historical baseline data to avoid cold-start instability.

• Incremental Online Learning: Models are configured for controlled online updates using small learning steps, sufficient statistics, or optimizer constraints to adapt safely to evolving player behavior.

• Partial Rollout: Only a subset of players (typically 20–30%) are exposed to the adaptive system initially.

• Metric Monitoring And Kill Switch: Engagement, retention, and revenue metrics are monitored continuously. If degradation is detected, the system is disabled and diagnosed before further rollout.

This approach prioritizes predictability, reversibility, and player safety over aggressive optimization.

Typical Model Selection

Regression

• Normal Linear Regression

• Bayesian Linear Regression

• Bayesian Quantile Linear Regression

• Passive Aggressive Regressor

Classification

• Passive Aggressive Classifier

• One-Class Passive Aggressive Classifier

Scope

Consulting is optional and does not replace the library. Studios may use DataPredict independently or engage for guidance depending on internal expertise and risk tolerance.

For licensing and consulting inquiries, please refer to the licensing documentation or contact me directly on DevForum.