MLOps
pytorch-deepdream
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| MLOps | pytorch-deepdream | |
|---|---|---|
| 2 | 3 | |
| 1,709 | 353 | |
| 10.4% | - | |
| 2.5 | 0.0 | |
| 9 months ago | 7 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
pytorch-deepdream
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
fastai - The fastai deep learning library
dvc - 🦉 ML Experiments and Data Management with Git
Deep-Learning-Experiments - Videos, notes and experiments to understand deep learning
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
python_autocomplete - Use Transformers and LSTMs to learn Python source code
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-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.
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.