[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit?

• nestedcvtraining

  6 27 0.0 Python

• summer

  4 7 0.0 Python

  A compartmental disease modelling framework (Python) (by monash-emu)

  

I'm starting to document a Python library with Sphinx and hosting it on a static site (eg. AWS S3, Netlify, Clouflare pages). Most of the docs are markdown with the examples being Jupyter notebooks. Docs are built and deployed to summerepi.com on commits to master. A little bit fiddly to set up but once it's going it's pretty magical. Source code is here.

• 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.

  

• clearml

  20 5,243 8.1 Python

  ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

  

• ploomber

  121 3,374 7.4 Python

  The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

  

You need a couple of tools to cover the entire ML lifecycle. For developing and deploying your pipelines, check out Ploomber (disclaimer: I'm the author):

• projects

  19 77 4.7 Jupyter Notebook

  Sample projects using Ploomber. (by ploomber)

  

You can convert a training pipeline into an online service easily (this is great to prevent training-serving skew). Here's an example project

• keepsake

  4 1,637 0.0 Python

  Version control for machine learning

  

Complementing the given answer, you could check https://github.com/replicate/keepsake for model versioning.

• speech-enhancement

  3 22 0.0 Python

  Experiments with speech enhancement (by MattSegal)

Even if detailed unit testing is hard, you can smoke test your models in CI to make sure that they're at least not crashing. More on smoke tests here. Some example smoke tests for a neural net here. Running your tests in GitHub Actions is relatively easy (here).

• 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.

  

• NumPy

  272 26,360 10.0 Python

  The fundamental package for scientific computing with Python.

  

Napoleon is a Sphinx extension that enables Sphinx to parse both NumPy and Google style docstrings - the style recommended by Khan Academy.

• Python Packages Project Generator

  5 1,064 0.0 Python

  Discontinued 🚀 Your next Python package needs a bleeding-edge project structure.

CookieCutter or Kedro are the winners. I still think we will stick to Kedro template, because it offers extra functionality, and I like to think of each project as a set of pipelines to be run. Anyway, some cookiecutter templates are very good, like this one. In case we use both Kedro and ClearML, we'll have to figure out how to integrate its pipelines with ClearML tasks. But in the slack channel of ClearML there are other teams doing the same, so at least it's possible.

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