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 * [Notebooks](https://github.com/rlan/notebooks) - A starter kit for Jupyter notebooks and machine learning. Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and Tensorflow) and CPU/CUDA versions.
 * [DVC](https://github.com/iterative/dvc) - Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML projects reproducible and shareable.
 * [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.
 * [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.
 * [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.