├── _config.yml ├── README.md └── index.md /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # mlops-case-study 2 | 3 | Welcome to MLOps Case Study 4 | This page collect MLOps case study to spread knolwge of MLOps. It might be help to Success your Machine Learning project! 5 | 6 | This page coverd MLOps & ML Enginnering & Applied DataScience. They are bound up in each other. 7 | -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | # Welcome to MLOps Case Study 2 | 3 | This page collects MLOps case study to spread knowledge of MLOps. 4 | It might be help to Success your Machine Learning project! 5 | 6 | This Page covers MLOps & ML Enginnering & Applied DataScience. 7 | They are bound up in each other. 8 | 9 | 10 | ## MLOps article 11 | - [MLOps](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) by GCP 12 | - [Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/) by Google 13 | - [Rules of Machine Learning: Best Practices for ML Engineering](https://developers.google.com/machine-learning/guides/rules-of-ml) in Google's best practices in machine learning. 14 | - [Linux Foundation AI](https://lfai.foundation/) 15 | - [The People + AI Guidebook](https://pair.withgoogle.com/guidebook/) was written to help user experience (UX) professionals and product managers follow a human-centered approach to AI. 16 | 17 | ### Machine Learning Platfrom and OSS 18 | - [Introducing FBLearner Flow: Facebook’s AI backbone](https://engineering.fb.com/ml-applications/introducing-fblearner-flow-facebook-s-ai-backbone/) 19 | - [Meet Michelangelo: Uber’s Machine Learning Platform](https://eng.uber.com/michelangelo-machine-learning-platform/) 20 | 21 | ## Company Blog & Publication 22 | - [Amazon Science](https://www.amazon.science/) 23 | - [Google AI](https://ai.google/) 24 | - [Etsy Data Science & Machine Learning ](https://www.dsml.etsy.com/publications) 25 | 26 | ## International Conference and Workshop of MLOps, MLEng and Applied DataScience 27 | 28 | ### Academic industry track 29 | - 2020 30 | - [ECNLP 2](https://sites.google.com/view/ecnlp/www-2020) in WWW2020 31 | - 2019 32 | - [SIGIR eCom2019](https://sigir-ecom.github.io/) 33 | - 2017 34 | - [Reliable Machine Learning in the Wild](https://sites.google.com/site/wildml2017icml/) in ICML 2017 Workshop 35 | - [Workshop on ML Systems](http://learningsys.org/nips17/acceptedpapers.html) at NIPS 2017 36 | - 2016 37 | - [Reliable Machine Learning in the Wild](https://sites.google.com/site/wildml2016nips/) in NIPS 2016 Workshop 38 | 39 | ### Industry 40 | 41 | - 2020 42 | - [OpML20](https://www.usenix.org/conference/opml20) 43 | - [Workshop on MLOps Systems](https://mlops-systems.github.io/) in MLSys2020 44 | - [MLSys2020](https://mlsys.org/) 45 | - [TWIMLcon](https://twimlcon.com/) 46 | - 2019 47 | - [Workshop on systems for ML at NeurIPS2019](http://learningsys.org/neurips19/) 48 | - [MLOpsNYC](https://www.mlopsnyc.com/) 49 | - [OpML19](https://www.usenix.org/conference/opml19) 50 | - [Qcon.ai 2019](https://qcon.ai/schedule/qconai2019/tabular) 51 | - [MLConf 2019 Sanfrancisco](https://mlconf.com/sessions/?event=mlconf-2019-sf&search=) 52 | 53 | ## Research paper 54 | 55 | - [What’s your ML test score? A rubric for ML production systems](https://research.google/pubs/pub45742/) 56 | 57 | ## Podcast 58 | - [twiml](https://twimlai.com/) 59 | - [MLOps.community](https://anchor.fm/mlops/) 60 | 61 | ### Support or Contact 62 | 63 | Having trouble with Pages? 64 | Please make issues :+1: 65 | --------------------------------------------------------------------------------