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1. [Nebullvm](https://github.com/nebuly-ai/nebullvm) - Easy-to-use library to boost deep learning inference leveraging multiple deep learning compilers.
2. [Netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models
2. [Jupyter Notebook](http://jupyter.org) - Web-based notebook environment for interactive computing
3. [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
4. [Visual Studio Tools for AI](https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/) - Develop, debug and deploy deep learning and AI solutions
5. [TensorWatch](https://github.com/microsoft/tensorwatch) - Debugging and visualization for deep learning
6. [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science.
7. [dowel](https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to `logger.log()`
8. [Neptune](https://neptune.ai/) - Lightweight tool for experiment tracking and results visualization.
9. [CatalyzeX](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension ([Chrome](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and [Firefox](https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
10. [Determined](https://github.com/determined-ai/determined) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
11. [DAGsHub](https://dagshub.com/) - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.
12. [hub](https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single numpy-like array on the cloud, accessible on any machine.
13. [DVC](https://dvc.org/) - DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
14. [CML](https://cml.dev/) - CML helps you bring your favorite DevOps tools to machine learning.
15. [MLEM](https://mlem.ai/) - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch processing.
16. [Maxim AI](https://getmaxim.ai) - Tool for AI Agent Simulation, Evaluation & Observability.
3. [Jupyter Notebook](http://jupyter.org) - Web-based notebook environment for interactive computing
4. [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
5. [Visual Studio Tools for AI](https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/) - Develop, debug and deploy deep learning and AI solutions
6. [TensorWatch](https://github.com/microsoft/tensorwatch) - Debugging and visualization for deep learning
7. [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science.
8. [dowel](https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to `logger.log()`
9. [Neptune](https://neptune.ai/) - Lightweight tool for experiment tracking and results visualization.
10. [CatalyzeX](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension ([Chrome](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and [Firefox](https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
11. [Determined](https://github.com/determined-ai/determined) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
12. [DAGsHub](https://dagshub.com/) - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.
13. [hub](https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single numpy-like array on the cloud, accessible on any machine.
14. [DVC](https://dvc.org/) - DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
15. [CML](https://cml.dev/) - CML helps you bring your favorite DevOps tools to machine learning.
16. [MLEM](https://mlem.ai/) - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch processing.
17. [Maxim AI](https://getmaxim.ai) - Tool for AI Agent Simulation, Evaluation & Observability.
18. [Deepnote](https://github.com/deepnote/deepnote) - Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.


### Miscellaneous
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