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Aquarium Learning helps deep learning teams improve their model performance by improving their datasets. Aquarium Learning wants to give everyone the same world-class ML tooling that big companies already have access to. They want to enable domain experts to build and deploy models without specialized ML expertise, making it easier to build and improve real-world ML products.
Neptune.ai is an experiment tracking hub bringing organization and collaboration to data science projects. Neptune records your entire experimentation process - exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. Everything is stored and backed-up in an organized knowledge repository, ready to be accessed, analyzed, shared, and discussed with your team. No matter what type of problems you are working on, Neptune fits them all, from evaluating credit risk to finding the nuclei in divergent images.
Snorkel AI started as a research project in the Stanford AI Lab in 2016, where they set out to explore a higher-level interface to machine learning through programmatically labeled and managed training data. From deploying early versions of Snorkel Flow's core technology with some of the world’s leading organizations, to empowering scientists, doctors, and journalists, they’ve seen firsthand how this approach democratizes and accelerates AI. Now, they’re working to bring their technology to everyone.
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