Skip to main content
MLOps logo

mlops

Open-source tool for tracking & monitoring machine learning models.

FastAPI React MongoDB Docker PyPI

PyPI version License

Table of Contents

Introduction

End-to-end machine learning projects require long-term lifecycles during which different models are evaluated, with various hyperparameters or data representations. Then, out of all the experiments, a final model must be selected for deployment in the production environment. There are some solutions available to manage the model creation process, such as mlflow or neptune.ai. However, none of them support the functionality of monitoring a deployed model in production.

As a part of the mlops project, we aim to create a ready-to-use tool for professionals in the Machine Learning industry allowing them not only to manage experiments during model creation process (tracking module), but also monitoring a deployed model working on real-world production data (monitoring module) with an option to setup email alerts using MailGun (email alerts module).

Technologies

Application consist of two main components:

Additionally, we use mongoDB database for storing tracking module data.

Installation & usage

To install the application locally, you need to have docker and docker-compose installed on your machine. Then, you can run the following command:

docker-compose up

After that you can access the application at http://localhost:3000.

To install the python package make sure you have Python >=3.9 installed on your machine. Then, you can install the package using pip:

pip install mlops-ai

Documentation

You can find the detailed documentation of the application here.

Examples

The main end-to-end notebook that presents key features of the package can be found here. Some other example notebooks are also provided inside the library/tests/notebooks directory.

License

Distributed under the open-source Apache 2.0 License. See LICENSE for more information.

Contact

Project authors are (in alphabetical order):

Feel free to contact us in case of any questions or suggestions.

References

This project was created as a final BE project of Computer Science course at Faculty of Mathematics and Computer Science of Adam Mickiewicz University.

To-Do

Application is still under development. Here is a list of features we plan to implement in the future:

  • Add support for the whole monitoring module
  • Add support for email alerts
  • AWS EC2 integration
  • Add support for multiple users (optionally)

Metadata

Release files for mlops-ai 1.3.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlops-ai 1.3.6
File Size Uploaded Provenance Actions
mlops_ai-1.3.6.tar.gz 13.5 kB Without provenance Details for mlops_ai-1.3.6.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for mlops-ai 1.3.6
File Interpreter ABI Platform Actions
mlops_ai-1.3.6-py3-none-any.whl Python 3 none any Details for mlops_ai-1.3.6-py3-none-any.whl

Total release size: 28.1 kB

Release files / mlops_ai-1.3.6.tar.gz

Download URL mlops_ai-1.3.6.tar.gz
Size 13.5 kB
Tags Source
SHA-256 checksum
How to use checksums
6a4629dc7b242ad375b56a38bf4ff7a36384941582a9562ef8a50b952e18ee52
BLAKE2b-256 checksum
How to use checksums
1b351369ef018f6cde3ca67b412a23ab10f8bca16bc4cf730d21b13d967c4848
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.12.3

Release files / mlops_ai-1.3.6-py3-none-any.whl

Download URL mlops_ai-1.3.6-py3-none-any.whl
Size 14.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
dfb0863e3153f872ef5cfcba911ea1a58ad184813b5cb3f55eedee038a3e4379
BLAKE2b-256 checksum
How to use checksums
ba35aecac643a8501a0f9011f144d68386a5cb3619131c3f805e6261e57aec21
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.12.3

Security for mlops-ai 1.3.6

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page