josephmisiti/awesome-machine-learning
1
1
Fork 0
mirror of https://github.com/josephmisiti/awesome-machine-learning.git synced 2024-11-22 20:28:32 +03:00
Code Issues Packages Projects Releases Wiki Activity

add Hamilton to tools

Browse Source
This commit is contained in:
zilto 2024-10-23 14:17:48 -04:00
parent 4964cff362
commit 484ceb05f9
1 changed files with 1 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -1794,6 +1794,7 @@ be
* [DVClive](https://github.com/iterative/dvclive) - Python library for experiment metrics logging into simply formatted local files. * [DVClive](https://github.com/iterative/dvclive) - Python library for experiment metrics logging into simply formatted local files.
* [VDP](https://github.com/instill-ai/vdp) - open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications. * [VDP](https://github.com/instill-ai/vdp) - open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
* [Kedro](https://github.com/quantumblacklabs/kedro/) - Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models. * [Kedro](https://github.com/quantumblacklabs/kedro/) - Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
* [Hamilton](https://github.com/dagworks-inc/hamilton) - a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
* [guild.ai](https://guild.ai/) - Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard. * [guild.ai](https://guild.ai/) - Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard.
* [Sacred](https://github.com/IDSIA/sacred) - Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard. * [Sacred](https://github.com/IDSIA/sacred) - Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
* [Comet](https://www.comet.com/) - ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to monitor their models in production. * [Comet](https://www.comet.com/) - ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to monitor their models in production.