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Posts tagged Data-centric AI
4 Types of ML Data Errors You Can Fix Right Now ⚡️
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.
🔭 Improving Your ML Datasets, Part 2: NER
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.
🔭 What is NER And Why It’s Hard to Get Right
In this post, we discuss the Named Entity Recognition (NER) task, why it is an important component of various NLP pipelines, and why it is particularly challenging to improve NER models.
🔭 Improving Your ML Datasets With Galileo, Part 1
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.
Being 'Data-Centric' is the Future of Machine Learning
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.
Fixing Your ML Data Blindspots
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.
Working with Natural Language Processing?
Read about Galileo’s NLP Studio
