Intro to Aquarium
Last updated
Last updated
Aquarium is an ML data operations platform that helps teams find issues, validate fixes, and add the right data to improve their machine learning datasets.
ML models are defined by a combination of code and the data. While there's a lot of great tools for debugging and understanding code, there's not a lot of tooling for debugging and understanding the actual data. Our interactive views and collaborative platform allows your teams to work more efficiently on data-centric workflows with the goal of:
Speeding up your ML workflows
Saving engineering time
Reducing operational risk
You should use Aquarium when you're trying to build or improve an ML model.
Most gains to model performance come from improving datasets rather than model code. And as a result, it's hard to make significant gains in the code without large time investments.
Thats where we come in! Aquarium can improve on your team's ML tasks like:
Assessing Data Quality
Find labeling errors and subsets of your data that have interesting/problematic patterns
Easy to use interface encourages other members in the process to get involved which leads to freeing up your ML engineers time
Comparing Model Performance
Diagnose the causes of critical model errors across multiple versions of your model
Compare multiple models with regression tests to ensure each iteration of your model is truly an improvement
Collecting Relevant Data
Quickly identify the highest value data to collect that improves the model performance
Reduce manual time spent trawling through unlabeled data to figure out what to label next
Teams have seen up to a 25% increase in model performance in a single cycle of dataset iteration with up to 8x less time spent than in their previous workflow!
Aquarium supports the following ML tasks:
Classification
2D Object Detection
3D Object Detection
Semantic Segmentation
If your team has a nuanced task or you work with a particular kind of data, feel free to reach out to our team here!
We provide a variety of quickstart guides to help you get familiar with our data upload process, and we also provide documentation around how to complete certain ML tasks and workflows.
In addition we have detailed documentation on how to upload different kinds of data listed here:
For further questions or scheduling time for a demo or discussion, you can reach out to Aquarium here!
Our website: http://aquariumlearning.com/
Our Hacker News launch post: https://news.ycombinator.com/item?id=23821502
Software 2.0: https://medium.com/@karpathy/software-2-0-a64152b37c35