Zero telemetry, fully local. It spawns `http-server` on port 2048 and opens your browser at `localhost`. Similar pattern as Jupyter Notebooks. Feel free to audit the code... the server is <200 LOC.
I definitely think that UX is an underappreciated area for machine learning data. I want to make a set of libraries and tools that make it easier for people to work with ML data in the browser. The first step of good data science is to become one with your data.
I started with parquet because most datasets for modern LLMs are in parquet format. But there are other formats like JSONL which are common too.
The rapid advancement of large language models (LLMs) like ChatGPT has captured headlines and imaginations. AI systems can now generate amazingly human-like text on any topic with just a few prompts.These behemoths, with their unparalleled capabilities, have necessitated a reevaluation of governance models. As organizations explore integrating LLMs into business operations, it’s crucial to implement governance measures enabling innovation while managing risks. As executives, understanding the transition from traditional machine learning governance to LLM-centric AI governance is crucial.
This is great, thanks for sharing. Key component in evolving FM based applications is making them feel as deterministic as possible vs probabilistic. Framework like this would enable generating trust in the outputs of these FMs.. exciting.
hi all creator here. We built this version of our product focused on dynamic data science teams that just wanted to be able to deploy, scale and run their models without worrying about ops. Some more details:
Hi doppenhe, we have that part already implemented using cml-send-github-check and dvc metrics diff. You can compare the metric that you prefer with dvc and then just set the status of the github check uploading your full report. Of course, you can also fail the workflow as your Github action does, but I think is more useful to see it as a report in the check.
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