Mlhbdapp New Apr 2026

# app.py from flask import Flask, request, jsonify import mlhbdapp

# Record metrics request_counter.inc() mlhbdapp.Gauge("inference_latency_ms").set(latency * 1000) mlhbdapp.Gauge("model_accuracy").set(0.92) # just for demo mlhbdapp new

return jsonify("sentiment": sentiment, "latency_ms": latency * 1000) The app is an open‑source (MIT‑licensed) web UI

If you’re a data‑engineer, ML‑ops lead, or just a curious ML enthusiast, keep scrolling – this post gives you a , a code‑first quick‑start , and a practical checklist to decide if the MLHB App belongs in your stack. 1️⃣ What Is the MLHB App? 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. | | How to get started | Install

# Install the SDK and the agent pip install mlhbdapp==2.3.0 # docker-compose.yml (copy‑paste) version: "3.9" services: mlhbdapp-server: image: mlhbdapp/server:2.3 container_name: mlhbdapp-server ports: - "8080:8080" # UI & API environment: - POSTGRES_PASSWORD=mlhb_secret - POSTGRES_DB=mlhb volumes: - mlhb-data:/var/lib/postgresql/data healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8080/health"] interval: 10s timeout: 5s retries: 5

@app.route("/predict", methods=["POST"]) def predict(): data = request.json # Simulate inference latency import time, random start = time.time() sentiment = "positive" if random.random() > 0.5 else "negative" latency = time.time() - start

(mlhbdapp) – What It Is, How It Works, and Why You’ll Want It (Published March 2026 – Updated for the latest v2.3 release) TL;DR | ✅ What you’ll learn | 📌 Quick takeaways | |----------------------|--------------------| | What the MLHB App is | A lightweight, cross‑platform “ML‑Health‑Dashboard” that lets developers and data scientists monitor model performance, data drift, and resource usage in real‑time. | | Why it matters | Turns the dreaded “model‑monitoring nightmare” into a single, shareable UI that integrates with most MLOps stacks (MLflow, Weights & Biases, Vertex AI, SageMaker). | | How to get started | Install via pip install mlhbdapp , spin up a Docker container, and connect your ML pipeline with a one‑line Python hook. | | What’s new in v2.3 | Live‑query notebooks, AI‑generated anomaly explanations, native Teams/Slack alerts, and an extensible plugin SDK. | | When to use it | Any production ML system that needs transparent, low‑latency monitoring without a full‑blown APM suite. |