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https://github.com/krisjin/ml-awesome
Machine learning awesome
https://github.com/krisjin/ml-awesome
List: ml-awesome
Last synced: 16 days ago
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Machine learning awesome
- Host: GitHub
- URL: https://github.com/krisjin/ml-awesome
- Owner: krisjin
- Created: 2020-12-24T03:10:36.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2020-12-31T09:33:03.000Z (almost 4 years ago)
- Last Synced: 2024-04-10T15:58:20.850Z (9 months ago)
- Size: 6.82 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - ml-awesome - Machine learning awesome. (Other Lists / PowerShell Lists)
README
# 机器学习相关资料
机器学习工程化，机器学习平台，MLOps
## 机器学习平台
- [Uber 机器学习平台 — 米开朗基罗](https://github.com/xitu/gold-miner/blob/master/TODO/meet-michelangelo-ubers-mechine-learning-plantform.md)
- [kleveross云原生的机器学习平台](https://github.com/kleveross)
- [Uber人工智能相关研究资料](https://eng.uber.com/research/?_sft_category=research-ai-ml)
- [Gallery: A Machine Learning Model Management System at Uber](http://openproceedings.org/2020/conf/edbt/paper_217.pdf)
- [MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis](https://dspace.mit.edu/bitstream/handle/1721.1/121346/sigmod_mistique.pdf?sequence=2&isAllowed=y)
- [Model Governance: Reducing the Anarchy of Production ML](https://www.usenix.org/system/files/conference/atc18/atc18-sridhar.pdf)
- [The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox](http://www.bailis.org/papers/velox-cidr2015.pdf)
- [azure机器学习平台文档中文](https://docs.azure.cn/zh-cn/machine-learning/)
- [Azure Databricks文档](https://docs.microsoft.com/en-us/azure/databricks/scenarios/what-is-azure-databricks)
- [Machine Learning Model Management in 2020 and Beyond – Everything That You Need to Know](https://neptune.ai/blog/machine-learning-model-management-in-2020-and-beyond)
- [美团一站式机器学习平台建设实践](https://tech.meituan.com/2020/01/23/meituan-delivery-machine-learning.html)
- [neptune](https://neptune.ai/blog)
- [MODELDB: A System for Machine Learning Model Management](https://www-cs.stanford.edu/~matei/papers/2016/hilda_modeldb.pdf)
- [Frameworks for Machine Learning Model Management](https://www.inovex.de/blog/machine-learning-model-management/)
- [On Challenges in Machine Learning Model Management](https://assets.amazon.science/7d/38/968b82c745bd9859a79dab0aade8/on-challenges-in-machine-learning-model-management.pdf)
- [A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning](https://github.com/EthicalML/awesome-production-machine-learning)
- [4-Steps to Machine Learning Model Management](https://community.hitachivantara.com/s/article/4-steps-to-machine-learning-model-management)
- [Machine Learning Pipeline: Architecture of ML Platform in Production](https://www.altexsoft.com/blog/machine-learning-pipeline/)
- [Runway: machine learning model experiment management tool](https://mlsys.org/Conferences/doc/2018/26.pdf)
- [解决部署AI系统的最后一公里问题](https://zhuanlan.zhihu.com/p/83159479)
- [Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform](https://arxiv.org/pdf/1810.07159.pdf)
- [acumos](https://github.com/acumos)
- [Model Governance: Reducing the Anarchy of Production ML](https://www.usenix.org/system/files/conference/atc18/atc18-sridhar.pdf)
- [Litz: Elastic Framework for High-Performance Distributed Machine Learning](https://www.usenix.org/system/files/conference/atc18/atc18-qiao.pdf)
- [m-loop](https://m-loop.readthedocs.io/en/latest/)
- [acumos document](https://docs.acumos.org/en/latest/index.html)
## MLOps
- https://github.com/microsoft/MLOps
- https://github.com/cdfoundation/sig-mlops/blob/master/roadmap/2020/MLOpsRoadmap2020.md
- https://twimlai.com/blog/
- https://algorithmia.com/
- https://www.coursera.org/lecture/mlops-fundamentals/concept-overview-Q4H0z
- https://docs.azure.cn/zh-cn/machine-learning/concept-designer
- https://ml-ops.org/
- https://github.com/microsoft/MLOps/tree/master/model-training
- [10 MLops platforms to manage the machine learning lifecycle](https://www.infoworld.com/article/3572442/10-mlops-platforms-to-manage-the-machine-learning-lifecycle.html)
- [grape up mlops](https://grapeup.com/services/mlops/)
- [MLOps 101: The Foundation for Your AI Strategy](https://www.datarobot.com/mlops-101/)
-[Machine Learning Life-Cycle](https://sites.usp.br/datascience/wp-content/uploads/sites/449/2019/08/SPSAS_Machine_Learning_Life-Cycle_Fabio_Porto.pdf)
## 搜索场景
- https://github.com/Microsoft/Recommenders