MLHB stands for Machine‑Learning Health‑Dashboard. The app is an open‑source (MIT‑licensed) web UI + API that aggregates telemetry from any ML model (training, inference, batch, or streaming) and visualises it in a health‑monitoring dashboard.
| Feature | Description | Typical Use‑Case |
|---------|-------------|------------------|
| Unified Metrics Panel | Real‑time charts for latency, error‑rate, throughput, GPU/CPU memory, and custom KPIs. | Spot performance regressions instantly. |
| Data‑Drift Detector | Statistical tests (KS, PSI, Wasserstein) + visual diff of feature distributions. | Alert when input data deviates from training distribution. |
| Model‑Quality Tracker | Track accuracy, F1, ROC‑AUC, calibration, and custom loss functions per version. | Compare new releases vs. baseline. |
| AI‑Explainable Anomalies (v2.3) | LLM‑powered “Why did latency spike?” narratives with root‑cause suggestions. | Reduce MTTR (Mean Time To Resolve) for incidents. |
| Alert Engine | Configurable thresholds → Slack, Teams, PagerDuty, email, or custom webhook. | Automated ops hand‑off. |
| Plugin SDK | Write Python or JavaScript plugins to ingest any metric (e.g., custom business KPIs). | Extend to non‑ML health checks (e.g., DB latency). |
| Collaboration | Shareable dashboards with role‑based access, comment threads, and export‑to‑PDF. | Cross‑team incident post‑mortems. |
| Deploy Anywhere | Docker image (mlhbdapp/server), Helm chart, or as a Serverless function (AWS Lambda). | Fits on‑prem, cloud, or edge environments. |
Bottom line: MLHB App is the “Grafana for ML” – but with built‑in data‑drift, model‑quality, and AI‑explainability baked in. mlhbdapp new
(Published March 2026 – Updated for the latest v2.3 release)
This report outlines the development plan for the "MLHBD App (New)," a comprehensive mobile and web application designed to integrate Mobile Laboratory services with Smart Health Bed monitoring systems. The new version aims to bridge the gap between in-patient monitoring and diagnostic lab results in real-time, providing healthcare professionals with a centralized dashboard for critical decision-making. MLHB stands for Machine‑Learning Health‑Dashboard
Date: October 26, 2023 Prepared For: Project Stakeholders / Management Project Lead: [Your Name/Team Name]
For the first time, the app now features an integrated lightweight IDE environment. Users can now write, commit, and push code directly within the MLHBDApp New interface without toggling between browser tabs. This feature supports syntax highlighting for over 30 languages and direct integration with repository pipelines. Bottom line: MLHB App is the “Grafana for
Based on the keyword "mlhbdapp new," this appears to be a request for a project report on a Mobile Lab/Health Bed Application (MLHBD App), focusing on the development of a new version or new module. "MLHBD" commonly refers to "Medical Lab/Health Bed" systems in healthcare IT contexts.
Below is a structured project report for the proposed new application.
MLHB stands for Machine‑Learning Health‑Dashboard. The app is an open‑source (MIT‑licensed) web UI + API that aggregates telemetry from any ML model (training, inference, batch, or streaming) and visualises it in a health‑monitoring dashboard.
| Feature | Description | Typical Use‑Case |
|---------|-------------|------------------|
| Unified Metrics Panel | Real‑time charts for latency, error‑rate, throughput, GPU/CPU memory, and custom KPIs. | Spot performance regressions instantly. |
| Data‑Drift Detector | Statistical tests (KS, PSI, Wasserstein) + visual diff of feature distributions. | Alert when input data deviates from training distribution. |
| Model‑Quality Tracker | Track accuracy, F1, ROC‑AUC, calibration, and custom loss functions per version. | Compare new releases vs. baseline. |
| AI‑Explainable Anomalies (v2.3) | LLM‑powered “Why did latency spike?” narratives with root‑cause suggestions. | Reduce MTTR (Mean Time To Resolve) for incidents. |
| Alert Engine | Configurable thresholds → Slack, Teams, PagerDuty, email, or custom webhook. | Automated ops hand‑off. |
| Plugin SDK | Write Python or JavaScript plugins to ingest any metric (e.g., custom business KPIs). | Extend to non‑ML health checks (e.g., DB latency). |
| Collaboration | Shareable dashboards with role‑based access, comment threads, and export‑to‑PDF. | Cross‑team incident post‑mortems. |
| Deploy Anywhere | Docker image (mlhbdapp/server), Helm chart, or as a Serverless function (AWS Lambda). | Fits on‑prem, cloud, or edge environments. |
Bottom line: MLHB App is the “Grafana for ML” – but with built‑in data‑drift, model‑quality, and AI‑explainability baked in.
(Published March 2026 – Updated for the latest v2.3 release)
This report outlines the development plan for the "MLHBD App (New)," a comprehensive mobile and web application designed to integrate Mobile Laboratory services with Smart Health Bed monitoring systems. The new version aims to bridge the gap between in-patient monitoring and diagnostic lab results in real-time, providing healthcare professionals with a centralized dashboard for critical decision-making.
Date: October 26, 2023 Prepared For: Project Stakeholders / Management Project Lead: [Your Name/Team Name]
For the first time, the app now features an integrated lightweight IDE environment. Users can now write, commit, and push code directly within the MLHBDApp New interface without toggling between browser tabs. This feature supports syntax highlighting for over 30 languages and direct integration with repository pipelines.
Based on the keyword "mlhbdapp new," this appears to be a request for a project report on a Mobile Lab/Health Bed Application (MLHBD App), focusing on the development of a new version or new module. "MLHBD" commonly refers to "Medical Lab/Health Bed" systems in healthcare IT contexts.
Below is a structured project report for the proposed new application.