Releases: raminmohammadi/MLOps
V1.2.0
What's Changed
- Lab2 Data Storage and Warehouse by @shaunkirthan in #53
- Lab-2 README updated by @shamhithk in #59
- Fix model directory issue in airflow tutorial by @Anuttan in #62
- Add composer beginner lab by @AshyScripts in #61
- Kubeflow Setup with Simple Task by @DRAJ6 in #49
New Contributors
Full Changelog: V1.1.0...V1.2.0
Contributors
Assets 2
V1.1.0
What's Changed
- Gcp compute engine lab 2 by @jkrishna2511 in #34
- Scaling vertical/horizontal & Advanced Deployment Strategies by @HeyIts-RJ in #35
- WIP Advance Lab for Airflow GCC by @aaditshah18 in #36
- Advance Lab for Airflow GCC Changes by @aaditshah18 in #38
- Gcp compute engine lab 3 by @jkrishna2511 in #37
- Network configuration on K8s by @HeyIts-RJ in #39
- Added deployment of model to vertex ai - Advance Lab Airflow by @aaditshah18 in #40
- Flight delay prediction lab/fake user simulator by @jkrishna2511 in #41
- Airflow Orchestration to Train/Retrain Model and Upload to GCS by @aaditshah18 in #42
- Added FastAPI code and K8s Manifest by @HeyIts-RJ in #43
- Updated Airflow lab-1 by @akhil189 in #44
- updated airflow Lab 2 by @akhil189 in #45
- Updated FastAPI Lab by @akhil189 in #46
- Updated Streamlit lab by @akhil189 in #47
- Data_Storage_and_Wearhouse_Lab by @shaunkirthan in #48
- Add new lab on GitHub Actions and GCP connections by @AshyScripts in #50
- Vertex ai by @rahulodedra30 in #55
- Vertex AI by @shamhithk in #52
- Revert "Vertex AI" by @raminmohammadi in #56
- KServe - Beginner Lab 1/2/3 - Intro to KServe and K8s Setup by @HeyIts-RJ in #54
- Vertex_AI by @shamhithk in #58
- Vertex ai (renamed folders) by @rahulodedra30 in #57
New Contributors
- @shaunkirthan made their first contribution in #48
- @AshyScripts made their first contribution in #50
- @rahulodedra30 made their first contribution in #55
- @shamhithk made their first contribution in #52
Full Changelog: release...V1.1.0
Contributors
Assets 2
Release version: v2024.1
Version 2024.1
Overview
We are pleased to announce the latest release of the MLOps Repository for 2024. This release includes updated labs, new exercises, and enhanced documentation to support the MLOps course at Northeastern University. Our goal is to provide a robust and comprehensive resource for students, instructors, and anyone interested in MLOps.
New Features and Updates
-
Updated Lab Content:
- Refreshed existing labs with the latest industry practices and tools.
- New lab exercises focused on advanced MLOps topics like data drift handling and continuous training.
-
Enhanced Documentation:
- Improved documentation for easier navigation and understanding.
- Detailed step-by-step instructions for each lab.
-
Additional Resources:
- New reading materials and references added.
- Links to relevant external resources for deeper learning.
-
Video Tutorials:
- Added new video tutorials to complement the lab exercises.
- Access videos on our Youtube channel.
Bug Fixes
- Resolved issues with outdated code samples.
- Fixed broken links in the documentation.
- Addressed compatibility issues with newer versions of libraries and tools.
Getting Started
To get started with the new labs and exercises:
- Clone this repository to your local machine.
- Navigate to the specific lab you are interested in.
- Read the lab instructions and review any accompanying documentation.
- Follow the provided code samples and examples to complete the lab exercises.
- Feel free to explore, modify, and experiment with the code to deepen your understanding.
For more detailed information on each lab and prerequisites, please refer to the lab's README or documentation.
Contributing
We welcome contributions to this repository. If you would like to contribute:
- Fork this repository.
- Create a branch for your changes.
- Make your changes and commit them with clear, concise messages.
- Test your changes to ensure they work as expected.
- Submit a pull request to the main repository.
Your contributions will help improve the overall quality of the labs and benefit the entire MLOps community.
License
This repository is open-source and is distributed under the Creative Commons License. Please review the license for more details on how you can use and share the content within this repository.
Acknowledgments
We would like to thank the students, instructors, and contributors who have provided valuable feedback and suggestions to improve this repository. Special thanks to Coursera for providing reading materials under the Creative Commons License.
For any questions or further information, please visit our Website.
Thank you for your continued support and contribution to the MLOps community.
Release Date: June 9, 2024