Linking pages
- GitHub - ripienaar/free-for-dev: A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev https://github.com/ripienaar/free-for-dev 80 comments
- GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software. https://github.com/josephmisiti/awesome-machine-learning 58 comments
- Increasing rate of experimentation - by Arkid Mitra https://arkid.substack.com/p/increasing-rate-of-experimentation 14 comments
- 8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem - neptune.ai https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem 6 comments
- GitHub - IDSIA/sacred: Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. https://github.com/IDSIA/sacred 6 comments
- How to Solve the Model Serving Component of the MLOps Stack https://alexandruburlacu.github.io/posts/2022-09-25-neptuneai-ml-serving 3 comments
- Tutorial on Graph Neural Networks for Computer Vision and Beyond | by Boris Knyazev | Medium https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d 3 comments
- GitHub - daefresh/awesome-data-temporality: A curated list to help you manage temporal data across many modalities 🚀. https://github.com/daefresh/awesome-data-temporality 3 comments
- GitHub - iterative/dvclive: 📈 Log and track ML metrics, parameters, models with Git and/or DVC https://github.com/iterative/dvclive 3 comments
- From Dev to Prod: a ML Pipeline Reference Post https://blog.dagworks.io/p/from-dev-to-prod-a-ml-pipeline-reference?r=2cg5z1 3 comments
- MLOps is part of DevOps. Not a fork — my thoughts on THE MLOps paper as an MLOps startup CEO | by Piotr Niedzwiedz | Oct, 2022 | Medium https://medium.com/@piotr.niedzwiedz/f324131709a7 2 comments
- Bringing software development principles to Machine Learning https://sachinchandra.substack.com/p/bringing-software-development-principles 2 comments
- From Dev to Prod: a ML Pipeline Reference Post https://blog.dagworks.io/p/from-dev-to-prod-a-ml-pipeline-reference 1 comment
- bnomial-archive/questions.md at master · akhildevelops/bnomial-archive · GitHub https://github.com/Enforcer007/bnomial-archive/blob/master/questions.md 0 comments
- GitHub - r0f1/datascience: Curated list of Python resources for data science. https://github.com/r0f1/datascience 0 comments
- GitHub - stared/livelossplot: Live training loss plot in Jupyter Notebook for Keras, PyTorch and others https://github.com/stared/livelossplot 0 comments
- Deep Learning for Image Segmentation: U-Net Architecture | by Ayyüce Kızrak, Ph.D. | Heartbeat https://heartbeat.fritz.ai/deep-learning-for-image-segmentation-u-net-architecture-ff17f6e4c1cf 0 comments
- GitHub - kelvins/awesome-mlops: A curated list of awesome MLOps tools https://github.com/kelvins/awesome-mlops 0 comments
- GitHub - ChristosChristofidis/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities. https://github.com/ChristosChristofidis/awesome-deep-learning 0 comments
- GitHub - academic/awesome-datascience: An awesome Data Science repository to learn and apply for real world problems. https://github.com/okulbilisim/awesome-datascience 0 comments
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