skoupidia
ml-pipeline-engineering
| skoupidia | ml-pipeline-engineering | |
|---|---|---|
| 1 | 2 | |
| 6 | 36 | |
| - | - | |
| 8.0 | 0.0 | |
| 20 days ago | almost 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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skoupidia
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Building a Deep Learning Rig
If you would like to put Kubernetes on top of this kind of setup this repo is helpful https://github.com/robrohan/skoupidia
The main benefit for me using it for my ML work loads is you can shutoff nodes entirely when you are not using them, then when you turn them back on they just rejoin the cluster.
It also helps managing different types of devices and workload (tpu vs gpu vs cpu)
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.
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
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. 📈