https://github.com/gbih/machine_learning
Notes about data science, machine-learning, data-engineering, MLOps, DevOps
https://github.com/gbih/machine_learning
data-engineering data-science devops keras kubernetes mlops tensorflow
Last synced: 2 months ago
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Notes about data science, machine-learning, data-engineering, MLOps, DevOps
- Host: GitHub
- URL: https://github.com/gbih/machine_learning
- Owner: gbih
- License: mit
- Created: 2022-07-20T06:04:46.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-11-08T15:28:01.000Z (over 3 years ago)
- Last Synced: 2025-06-14T21:46:06.694Z (about 1 year ago)
- Topics: data-engineering, data-science, devops, keras, kubernetes, mlops, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 45.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: license
Awesome Lists containing this project
README
# Notes about various data science, machine-learning, deep-learning APIs
## Notebooks
Reference, Guides, Tutorials:
* [TensorFlow Extended (TFX)](/tfx)
* [TFRecord, tf.train.Example](/tf_record_tftrain)
* [tf.Transform API](/tf_transform)
* [TensorFlow Serving](/tf_server)
* [TensorFlow Probability](tf_probability)
Book Material:
* [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](/book_hands_on)
- Excellent foundation for both Scikit-Learn and Deep Learning, probably the best single resource there is.
* [Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow](/book_building_ml_pipelines)
- Good introduction to TFX, though a bit behind the latest API versions.
* [Bayesian Methods for Hackers](/book_probablistic_programming)
- Great introduction to probabilistic programming, especially the Bayesian approach.
---
## Useful papers
[Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://arxiv.org/abs/2205.02302)
[Challenges in Deploying Machine Learning: a Survey of Case Studies, v3 (2022)](https://arxiv.org/abs/2011.09926)
[TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (2017)](https://research.google/pubs/pub46484/)
[Data Validation for Machine Learning (2019)](https://research.google/pubs/pub47967/)
[Hidden Technical Debt in Machine Learning Systems (2015)](https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html)
[Hidden technical debt in machine learning systems (日本語資料)](https://www.slideshare.net/Gushi/hidden-technical-debt-in-machine-learning-systems)
[Machine Learning: The High Interest Credit Card of Technical Debt (2014)](https://research.google/pubs/pub43146/)
[AutoGraph: Imperative-style Coding with Graph-based Performance (2019)](https://research.google/pubs/pub47990/)
[TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines (2020)](https://dl.acm.org/doi/abs/10.1145/3318464.3384707)
[Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX) (2021)](https://arxiv.org/abs/2010.02013)
[tf.data: A Machine Learning Data Processing Framework (2021)](https://arxiv.org/abs/2101.12127)
[TensorFlow: A system for large-scale machine learning (2016)](https://arxiv.org/abs/1605.08695)