This week was highly productive across all subteams, with significant advancements in UI, feature engineering (FE), and machine learning (ML). Each team made progress toward refining and optimizing their respective components, bringing us closer to a more streamlined and efficient system.

The UI team successfully finalized the statistics for the site-specific component, ensuring better visualization and interaction for users. Additionally, the team fully implemented filters, allowing filtering by date and anomaly count. Another notable improvement was the update to marker colors and hover effects based on anomaly count, enhancing the user experience and data interpretation.

The FE team focused on refining feature selection through correlation analysis, ultimately reducing the number of features to ten for the baseline model. They also conducted rigorous testing on models with and without feature engineering to evaluate their impact on performance.

The ML team made substantial progress by merging the ML pipeline with multisite preprocessing. They finalized both individual and ensemble multisite insight/champs pipelines, ensuring better model integration. The prediction scripts were also updated to accommodate synthetic data, output results to a file, and display relevant model statistics. Additionally, the team trained the seq2seq unified/dcr model, with work on secure_user still in progress.

Looking ahead to next week, the UI team will shift focus to preparing for usability testing, ensuring the latest improvements meet user needs effectively. The FE team plans to test and potentially reintroduce some eliminated features based on performance analysis. Meanwhile, the ML team will focus on Optuna tuning and testing for atomic models and work on integrating the end-to-end ensemble preprocessing and prediction pipeline.

Week by week, we are making great strides in refining our system. Looking forward to another week of progress—until next time!