Radicalbit AI Monitoring

👋 Welcome!

The Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring your Artificial Intelligence models in production.

🤔 Why Monitor AI Models?

While models often perform well during development and validation, their effectiveness can degrade over time in production due to various factors like data shifts or concept drift. The Radicalbit AI Monitor platform helps you proactively identify and address potential performance issues.

🗝️ Key Functionalities

The platform provides extensive monitoring capabilities to ensure optimal performance of your AI models in production. It analyzes both your reference dataset (used for pre-production validation) and the current datasets, allowing you to put under control:

• Data Quality
• Model Quality
• Model Drift

🏗️ Repository Structure

This repository contains all the files and projects to run Radicalbit AI Monitoring Platform

• ui
• api
• sdk
• spark

🚀 Installation using Docker compose

In this repository a docker compose file is available to run the platform in local with a K3s cluster where we can deploy Spark jobs.

To run, simply:

docker compose up

If the UI is needed:

docker compose --profile ui up

After all containers are up & running, you can go to http://localhost:5173 to play with the app.

Interacting with K3s cluster

In the compose file is present a k9s container that can be used to monitor the K3s cluster.

docker compose up k9s -d && docker attach radicalbit-ai-monitoring-k9s-1

Other tools

In order to connect and interact with the K3s cluster from the local machine (for example with Lens or kubectl) is necessary to create another file starting from ./docker/k3s_data/kubeconfig/kubeconfig.yaml (that is automatically generated when the docker compose is up and running).

Copy the above file and modify https://k3s:6443 with https://127.0.0.1:6443 and use this new file to interact with the cluster from the local machine

Real AWS

In order to use a real AWS instead of Minio is necessary to modify the environment variables of the api container, putting real AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION and S3_BUCKET_NAME and removing S3_ENDPOINT_URL.

Teardown

To completely clean up the environment we can use docker compose

docker compose --profile ui --profile k9s down -v --remove-orphans

To remove everything including container images:

docker compose --profile ui --profile k9s down -v --remove-orphans --rmi all

📖 Documentation

You can find the following documentation:

• An extensive step-by-step guide to install the development/testing version of the platform.
• A guide that walks users through creating dashboards on the platform.

🤝 Community

Please join us on our Discord server, to discuss the platform, share ideas, and help shape its future! Get help from experts and fellow users.

📦 Functionalities & Roadmap

We've released a first dashboard, covering Binary Classification models for tabular data. Over the coming weeks, we will be adding the following functionalities to the platform:

• Batch workloads

  • Binary Classification (Tabular Data)
  • LLMs (Data Quality)
  • LLMs (Model Quality)
  • Multiclass Classification (Tabular Data)
  • Regression (Tabular Data)
  • Computer Vision (Images)
  • Clustering (Tabular Data)

• Real-Time workloads

  • Binary Classification
  • Multiclass Classification
  • Regression
  • Computer Vision
  • Clustering