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cleanlab
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
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grape
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations (by AnacletoLAB)
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awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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How about cleanlab? It works for any data you can train a classifier or get embeddings on (text, tabular, image, audio, etc). We just released some new features as well. Currently, cleanlab can automatically:
You definitely forgot https://www.kern.ai/ :)
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning
Thanks for the kind words! Make sure to check out the current open MIT course if you are just starting out: https://dcai.csail.mit.edu/
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
The deodel classifier can act as a quick dataset evaluation tool. If your data is available in table format, you can check its potential for prediction/classification. Just feed it to deodel. It accepts mixed attributes without any preliminary curation. It simply considers attribute values expressed as floats (dot decimal) as being continuous. It accepts even a mix of continuous and categorical values for the same attribute column.