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radicalbit-ai-monitoring

radicalbit/radicalbit-ai-monitoring

Comprehensive solution for AI model monitoring in production

GraphCanon updated Sep 10, 2026 · GitHub synced Sep 10, 2026

26views this month

92 stars11 forksLast push Jun 15, 2026 Python Apache-2.0

Decision brief

radicalbit-ai-monitoring provides a Docker Compose-based platform for monitoring AI models in production with support for K3s and Spark job deployments.

Good fit when

  • When you require a comprehensive solution that supports both machine learning observability and data drift detection deployed through Docker Compose setup.
  • If your infrastructure is already set up to use K3s, this tool can seamlessly integrate into your existing environment for monitoring AI models.

Avoid when

  • When your deployment does not support or plan to avoid using Docker Compose and K3s for running Spark jobs.
  • In cases where a more specific solution is needed that focuses solely on one aspect of observability, rather than this comprehensive approach with AI model monitoring.
Requirements:
Requires Docker Compose for local deployment setup and K3s support to deploy Spark jobs.

Observed Jul 16, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Steady (86d since push)
As of Sep 10, 2026
Provenance
Not a fork · Organization account
As of Sep 10, 2026
Security (OSV)
No lockfile
As of Jul 15, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install radicalbit-ai-monitoring
PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

radicalbit/radicalbit-ai-monitoring offers a platform to monitor and observe artificial intelligence models through a Docker Compose setup with support for K3s.

Capability facts

Languages
python

Source: github.language · Sep 10, 2026

Categories

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README

🚀 Installation using Docker compose This repository provides a Docker Compose file for running the platform locally with a K3s cluster. This setup allows you to deploy Spark jobs. To run, simply: If the UI is needed: In order to initialize the platform with demo models you can r...

For agents

This page has a .md twin and JSON over the API.

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