sourabh0394agr

3 ms·

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by sourabh0394agr 4y ago
Really sorry about these comments. This is not something we intended. We did share about our post with few family and friends but we didn't realise it would translate this way. We are building our project very passionately and want tru
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by sourabh0394agr 4y ago
Thanks for starring! We are already working on an example to fine-tune a LLM (using Bert from Huggingface) by doing sentiment analysis on the model outputs (filtering output cases where user feedback has a negative sentiment and fine-tuning
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by sourabh0394agr 4y ago
The former i.e. it runs the detection logic in background on the machine itself where the model predictions are happening. Currently we support running simple clustering algos but are working to enable even running simple Neural Nets as par
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by sourabh0394agr 4y ago
We support multiple statistical measures such as Earth moving distance, KL Divergence, Jensen Shannon distance etc. and are continuously adding more. Each of these measures work well for different types of ML models: you can also read more
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by sourabh0394agr 4y ago
Great question - Problematic data-points are essentially the cases where your model is not performing well. Now, we have three ways to find them: 1. Statistical tools: We perform clustering on your training dataset and identify cases in pro
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by sourabh0394agr 4y ago
Our tool supports a wide variety of ML models (both Deep learning based as well as classical ones, except for Video CNNs). Below are some of the sample use-cases: 1. LLMs: UpTrain tracks unseen prompts, logs model performance, and detects p
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by sourabh0394agr 4y ago
Great question - couple of differences: 1. We are open-source & self-hosted, while most of the existing solutions are closed Saas tools and many of them ask you to send your data to their servers for analysis 2. We focus a lot on custom
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Show HN: UpTrain – Open-source ML observability and refinement tool(github.com)
88 points by sourabh0394agr 4y ago|14 comments