Mlhbdapp New ^hot^ Official

| Feature | Description | Typical Use‑Case | |---------|-------------|------------------| | | 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. |

: Built entirely on an eye-strain-reducing dark interface to accommodate long browsing sessions. mlhbdapp new

Before installing, verify the MD5 checksum provided on the download page. For version 3.0+ (the "New" branch), the hash should start with 7F3A9B... A mismatch indicates a corrupted or malicious file. | Feature | Description | Typical Use‑Case |