cml VS spotlight

Continuous Machine Learning (CML) is an open-source command-line interface tool designed to enhance continuous integration and delivery (CI/CD) workflows, with a focus on Machine Learning Operations (MLOps). The tool facilitates automated development workflows, including machine provisioning, model training and evaluation, comparing machine learning experiments across your project’s history, and monitoring changing datasets.

If run into trouble feel free to open an issue on Github: https://github.com/Renumics/spotlight

Hey HN,

when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem.

That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code.

Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes.

Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the hub.

You can find more info on the Hugging Face blog: https://huggingface.co/blog/scalable-data-inspection

Link to repo: https://github.com/Renumics/spotlight

Happy to hear your feedback!

Renumics Spotlight is a powerful tool for intuitively exploring unstructured datasets directly from dataframes. It’s designed to simplify data comprehension, allowing users to create interactive visualizations and leverage data enrichments like embeddings, predictions, and uncertainties to identify critical data clusters.

I've been working on our interactive dataset explorer for machine learning: renumics-spotlight. Recently, I set up an image classification example using it. I utilized Bing image search to create a fully functional example for custom-defined classes.

That is why we developed Spotlight: https://github.com/Renumics/spotlight