“The data I work with is always clean, error free, with no hidden biases” said no one that has ever worked on training and productionizing ML models. Learn what ML data Intelligence is and how Galileo can help with your unstructured data.
When working on machine learning (ML) projects, the challenges are usually centered around datasets in both the pre-training and post-training phases, with their own respective issues that need to be addressed. Learn about different ML data blind spots and how to address these issues.
Build better models, faster, with better data. We will dive into what is ML data intelligence, and it's 5 principles you can use today.
Data is critical for ML. But it wasn't always this way. Learn about how focusing on ML Data quality came to become the central figure for the best ML teams today.
We used Galileo on the popular Newsgroups dataset to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available publicly for use.
In this article, Galileo founding engineer Nikita Demir discusses common data errors that NLP teams run into, and how Galileo helps fix these errors in minutes, with a few lines of code.
Machine Learning is advancing quickly but what is changing? Learn what the state of ML is today, what being data-centric means, and what the future of ML is turning into.
One neglected aspect of building high-quality models is that it depends on one crucial entity: high quality data. Good quality data in ML is the most significant impediment to seamless ML adoption across the enterprise.
Putting a high-quality Machine Learning (ML) model into production can take weeks, months, or even quarters. Learn how ML teams are now working to solve these bottlenecks.
We used Galileo on the popular MIT dataset with a NER task, to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available for use.
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