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https://github.com/MG-Microsoft/MLOps_Workshop
Azure MLOps
https://github.com/MG-Microsoft/MLOps_Workshop
azure azure-devops azure-machine-learning devops machine-learning mlops mlops-workflow
Last synced: 3 months ago
JSON representation
Azure MLOps
- Host: GitHub
- URL: https://github.com/MG-Microsoft/MLOps_Workshop
- Owner: MG-Microsoft
- License: mit
- Created: 2021-05-20T10:43:37.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-21T13:07:42.000Z (11 months ago)
- Last Synced: 2024-07-29T17:04:48.351Z (6 months ago)
- Topics: azure, azure-devops, azure-machine-learning, devops, machine-learning, mlops, mlops-workflow
- Language: Python
- Homepage:
- Size: 249 KB
- Stars: 105
- Watchers: 5
- Forks: 248
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](LICENSE)
# DevOps For Machine Learning | MLOps
This repository is created by [Mohammad Ghodratigohar]( https://www.linkedin.com/in/mohammad-ghodratigohar/) for hands-on MLOps workshop using [Azure Machine Learning]( https://docs.microsoft.com/en-us/azure/machine-learning/) and [Azure DevOps]( https://docs.microsoft.com/en-us/azure/devops/?view=azure-devops&viewFallbackFrom=vsts).
Complete implementation and explanation of this repository is recorded in these 10 part tutorial video series:
[Video Series Playlist](https://www.youtube.com/playlist?list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f)
[Part1](https://www.youtube.com/watch?v=-QxwB7PoSdA&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=3),
[Part2](https://www.youtube.com/watch?v=Gzjr716RU9g&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=3),
[Part3](https://www.youtube.com/watch?v=L-nIreup0HQ&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=1),
[Part4](https://www.youtube.com/watch?v=b15l4BLAnmc&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=5),
[Part5](https://www.youtube.com/watch?v=C79hIHRBSsQ&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=5),
[Part6](https://www.youtube.com/watch?v=rPowmr43kzc&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=6),
[Part7](https://www.youtube.com/watch?v=iq4hGqC_JMs&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=7),
[Part8](https://www.youtube.com/watch?v=p9CxWhpE4uQ&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=8),
[Part9](https://www.youtube.com/watch?v=y9NMFLBo3bQ&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=9),
[Part10](https://www.youtube.com/watch?v=KHD2oyP8W94&list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f&index=10)
For any further inquiries or questions, please contact me at [email protected] .
## MLOps Workflow
Machine Learning Operations ([MLOps]( https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment)) is based on DevOps principles and practices that increase the efficiency of workflows.
This repository contains codes and guidelines for configuring the MLOps workflow with Azure as shown below:
## MLOps with Azure Machine Learning
Azure Machine Learning provides the following MLOps capabilities:
- **Machine Learning pipelines** allow you to define repeatable and reusable steps for your data preparation, training, and scoring processes.
- **Create reusable software environments** for training and deploying models.
- **Register, package, and deploy models** from anywhere. You can also track associated metadata required to use the model.
- **Capture the governance data** for the end-to-end ML lifecycle. The logged information can include who is publishing models, why changes were made, and when models were deployed or used in production.
- **Notify and alert on events in the ML lifecycle**. For example, experiment completion, model registration, model deployment, and data drift detection.
- **Monitor ML applications for operational and ML-related issues**. Compare model inputs between training and inference, explore model-specific metrics, and provide monitoring and alerts on your ML infrastructure.
- **Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines**. Using pipelines allows you to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services.
