MLOps
MachineLearningNotebooks
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| MLOps | MachineLearningNotebooks | |
|---|---|---|
| 2 | 2 | |
| 1,709 | 3,951 | |
| 10.4% | 1.3% | |
| 2.5 | 6.4 | |
| 9 months ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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
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Deploying Azure Machine Learning Models to Prod Environments
Walk through this, it shows how to operationalise your ML pipeline https://github.com/Microsoft/MLOps
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[D] How to maintain ML models?
Maybe something like this: https://github.com/microsoft/MLOps
MachineLearningNotebooks
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Multiple model loading on a Online Fully managed endpoint
I found an example using the python SDK v2:
- I Took The Azure DP-100 exam today and passed it
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
dvc - 🦉 ML Experiments and Data Management with Git
One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
mlops-v2 - Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
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.
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
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.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
awesome-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.
feature-engineering-tutorials - Data Science Feature Engineering and Selection Tutorials