[D] How to maintain ML models?

• dvc

  109 13,116 9.7 Python

  🦉 ML Experiments and Data Management with Git

  

DVC is a useful (git for data & models) https://dvc.org/ tool.

• MLflow

  56 17,234 9.9 Python

  Open source platform for the machine learning lifecycle

  

MLflow is a MLOps tool that may help you: https://mlflow.org/

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• MLOps

  2 1,704 2.5 Jupyter Notebook

  MLOps examples

  

Maybe something like this: https://github.com/microsoft/MLOps

• awesome-seml

  1 1,194 0.0

  A curated list of articles that cover the software engineering best practices for building machine learning applications.

  

They also have an awesome-seml repo on GitHub outlining many (scientific) articles as well as tools and frameworks that may help you out in implementing these best practices.

• mllint

  3 72 3.8 Go

  `mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.

Finally, there is the mllint tool that I have been developing during my MSc thesis on Software Quality in ML projects. While still a research prototype, it can already analyse your project and may be able to provide you with practical recommendations on what tools & techniques to employ for several aspects of your ML project's development. Feel free to try it out on your project and let me know what you think of it!

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

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