Data processing

[Prateekkp]

This pull request significantly updates the architecture documentation for GridCast, providing a much more detailed and production-focused description of the system’s pipeline, components, and data flows. The changes clarify the dual-model (XGBoost + LSTM) approach, add detailed artifact and data sync layers, and enhance the diagrams and sequence flows to reflect the current implementation. The documentation now also emphasizes artifact-based, deterministic serving and production reliability, and outlines future extensibility.

Pipeline and Model Architecture Updates

• Replaced the generic "Modeling" and "Serving" layers with explicit "Feature Engineering," "Dual Model Training (XGBoost + LSTM)," "Forecast Artifact Generation," and "Static Artifact Publishing" layers, reflecting the current dual-model, artifact-based approach.
• Updated the architecture and sequence diagrams to show the dual-model training, forecast generation, JSON artifact publishing, and data sync steps, including the flow from raw data to the Next.js dashboard. [1] [2] [3]

Model and Artifact Details

• Expanded the model training section to describe both XGBoost and LSTM models, including their respective responsibilities, training horizons, and validation strategies.
• Detailed the structure and contents of model artifact storage, specifying file formats and directory layout for both XGBoost and LSTM artifacts.

Artifact-Based Serving and Data Sync

• Introduced a dedicated "Forecast Generation Layer" and "JSON Artifact Publishing Layer," describing how forecasts and metrics are pre-generated and served as static files for reproducibility and offline operation.
• Added a "Data Sync Layer" to handle mirroring of artifacts from backend to frontend, ensuring the Next.js dashboard always serves up-to-date forecasts.

Visualization and Frontend

• Updated the visualization layer to describe the Next.js React dashboard, its features (dual-model comparison, KPIs, residual heatmaps, CSV export, authentication), and the technology stack used.

Design Principles and Future Enhancements

• Added sections on modular design, artifact-based deterministic serving, dual-model comparison, observability, production reliability, and outlined current limitations and future architecture enhancements (e.g., streaming, multi-region, model registry, containerization, A/B testing).

These updates provide a comprehensive, clear, and production-ready overview of the GridCast system, making it easier for engineers and stakeholders to understand the pipeline and its extensibility.

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Project	Deployment	Actions	Updated (UTC)
grid-cast	Ready	Preview, Comment	May 10, 2026 3:54pm