Linking pages
- Why We Need DevOps for ML Data | Tecton https://tecton.ai/blog/devops-ml-data/ 87 comments
- GitHub - visenger/awesome-mlops: A curated list of references for MLOps https://github.com/visenger/awesome-mlops 2 comments
- Machine learning deserves its own flavor of Continuous Delivery | by Derek Haynes | Towards Data Science https://towardsdatascience.com/machine-learning-deserves-its-own-flavor-of-continuous-delivery-f4d1e76e6b69 0 comments
- GitHub - HuaizhengZhang/Awesome-System-for-Machine-Learning: A curated list of research in machine learning systems (MLSys). Paper notes are also provided. https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning 0 comments
- Machine learning deserves its own flavor of Continuous Delivery | Booklet.ai https://booklet.ai/blog/continuous-delivery-machine-learning-cd4ml/ 0 comments
- GitHub - kelvins/awesome-mlops: A curated list of awesome MLOps tools https://github.com/kelvins/awesome-mlops 0 comments
- GitHub - poga/awesome-federated-learning: resources about federated learning and privacy in machine learning https://github.com/poga/awesome-federated-learning 0 comments
- Test-Driven Machine Learning Development (Deployment Series: Guide 07) - ML in Production https://mlinproduction.com/testing-machine-learning-models-deployment-series-07/ 0 comments
- Software 2.0 vs Software 1.0 - by Karim Fanous - Cumulative https://karimfanous.substack.com/p/software-20-vs-software-10 0 comments
- MLOps with a Feature Store. If AI is to become embedded in the DNA… | by Jim Dowling | Towards Data Science https://towardsdatascience.com/mlops-with-a-feature-store-816cfa5966e9 0 comments
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