MLOps
mlops-with-vertex-ai
| MLOps | mlops-with-vertex-ai | |
|---|---|---|
| 2 | 2 | |
| 1,734 | 333 | |
| 7.7% | 3.3% | |
| 2.5 | 0.0 | |
| 10 months ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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
-
Deploying Azure Machine Learning Models to Prod Environments
Walk through this, it shows how to operationalise your ML pipeline https://github.com/Microsoft/MLOps
-
[D] How to maintain ML models?
Maybe something like this: https://github.com/microsoft/MLOps
mlops-with-vertex-ai
-
Where to start?
To take it to the next level, maybe start to dive into a truer "end-to-end mlops" example.
- GitHub - GoogleCloudPlatform/mlops-with-vertex-ai: An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
dvc - 🦉 ML Experiments and Data Management with Git
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
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.
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
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.
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
awesome-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
MachineLearningNotebooks - Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
generative-ai - Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI