ml-pipeline-engineering
ai_book
| ml-pipeline-engineering | ai_book | |
|---|---|---|
| 2 | 1 | |
| 36 | 19 | |
| - | - | |
| 0.0 | 8.7 | |
| almost 2 years ago | 8 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
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ml-pipeline-engineering
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Engineering ML Pipelines - Part 2 of 3
Part One was all about getting setup and ready for the main event that is Part Two - developing the pipeline:
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Engineering ML Pipelines - Part 1 of 3
The GitHub repo that accompanies this project will have one branch for each post in the series, so you can see how it develops.
ai_book
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
biggestwar_ai
bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
pytorch-serving-workshop - Slides and notebook for the workshop on serving bert models in production
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈