Road map for the release 0.7

• Power model training pipeline
• Train power model from various machines
  • SPECPower: [plus] large amount (>900), [minus] offline dataset with simple metric (CPU utilization)
  • AWS self-hosted instance: [plus] limited number of profiles (40), [minus] on-release kepler metrics (CPU, cache, and so on)
• Model selection logic with machine similarity report
• Power model continuous integration and delivery framework
• Regression testing

Local XGBoost estimator

• Migrate xgboost to the main pipeline abstract (removal of XGBoostRegressionStandalonePipeline)
• Train the model and export weight
• Test xgboost kepler integration with provided weight

Page cache

• Collect data with page cache hit
• Update feature group and retrain the model

Model accuracy

• Add MAPE and change error key to MAPE
• Improve training with feature relevance knowledge (such as not using memory feature for core model training)

Integration and Deployment

• Training CI - Tekton
• Platform validation CI
• Operator integration