MLOps vs awesome-seml

MLOps

MLOps examples (by microsoft)

Mlops azureml

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awesome-seml

A curated list of articles that cover the software engineering best practices for building machine learning applications. (by SE-ML)

Awesome awesome-list software-engineering Machine Learning Deep Learning ml-ops

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MLOps		awesome-seml
	Project
2	Mentions	1
1,709	Stars	1,195
10.4%	Growth	1.3%
2.5	Activity	0.0
9 months ago	Latest Commit	about 1 month ago
Jupyter Notebook	Language
MIT License	License	Creative Commons Zero v1.0 Universal

The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

MLOps

Posts with mentions or reviews of MLOps. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-16.

Deploying Azure Machine Learning Models to Prod Environments
1 project | /r/azuredevops | 6 Nov 2021

Walk through this, it shows how to operationalise your ML pipeline https://github.com/Microsoft/MLOps
[D] How to maintain ML models?
5 projects | /r/MachineLearning | 16 Sep 2021

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

awesome-seml

Posts with mentions or reviews of awesome-seml. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-16.

[D] How to maintain ML models?
5 projects | /r/MachineLearning | 16 Sep 2021

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.

What are some alternatives?

When comparing MLOps and awesome-seml you can also consider the following projects:

MLflow - Open source platform for the machine learning lifecycle

yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

dvc - 🦉 ML Experiments and Data Management with Git

mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI

pytorch-deepdream - PyTorch implementation of DeepDream algorithm (Mordvintsev et al.). Additionally I've included playground.py to help you better understand basic concepts behind the algo.

mllint - `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.

awesome-vulnerability-assessment - An ever-growing list of resources for data-driven vulnerability assessment and prioritization

MachineLearningNotebooks - Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.