Data Error Potential -- Quantifiably identify the data your models struggle with
April 18 2023

Introducing Data Error Potential (DEP) - New Powerful Metric for Quantifying Data Difficulty for a Model

The Data Error Potential (DEP) is a 0 to 1 score that provides a tool to very quickly sort and bubble up data that is most difficult and worthwhile to explore when digging into your model’s errors. Since DEP is task agnostic, it provides a strong metric to guide exploration of model failure modes.

Jonathan Gomes Selman
Jonathan Gomes SelmanFounding Engineer
Data QualityData Errors

Recent Posts

Label Studio and Galileo
March 26 2023

LabelStudio + Galileo: Fix your ML data quality 10x faster

Galileo integrates deeply with Label Studio to help data scientists debug and fix their training data 10x faster.

Data LabelingIntegration
Galileo Console surfacing errors on ImageNet
March 20 2023

ImageNet Data Errors Discovered Instantly using Galileo

Using Galileo you can surface labeling errors and model errors on the most popular dataset in computer vision. Explore the various error type and simple visualization tools to find troublesome data points.

Computer vision
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February 14 2023

Webinar - Unpacking The State of Data Quality in Machine Learning

Unpack the findings of our State of Machine Learning Data Quality Report. We have surveyed 500 experienced data professionals to learn what types of data they work with, what data errors they encounter, and what technologies they use.

Data Quality
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February 14 2023

Free Machine Learning Workshop - Build Higher Quality Models with Higher Quality Data

Learn how to instantly resolve data errors using Galileo. Galileo Machine Learning Data Quality Intelligence enables ML Practitioners to resolve data errors.

Data Quality
NLP: Huggingface Transformers NER, understanding BERT with Galileo
February 02 2023

NLP: Huggingface Transformers NER, understanding BERT with Galileo

HuggingFace has proved to be one of the leading hubs for NLP-based models and datasets powering so many applications today. But in the case of NER, as with any other NLP task, the quality of your data can impact how well (or poorly) your models perform during training and post-production.

Huggingface
December 29 2022

Building High-Quality Models Using High Quality Data at Scale

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.

Data QualityData ErrorsML Data Intelligence
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.
December 20 2022

How to Scale your ML Team’s Impact

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.

ML Data Intelligence
December 08 2022

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.

ML Data IntelligenceData-centric AIData Errors
November 27 2022

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.

ML Data IntelligenceData-centric AI
October 03 2022

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.

Data-centric AINLPML Data Intelligence
September 20 2022

5 Principles You Need To Know About Continuous ML Data Intelligence

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.

NLPML Data Intelligence
September 08 2022

“ML Data” : The past, present and future

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.

NLPML Data Intelligence
June 07 2022

🔭 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.

NLPNERData-centric AIML Data Intelligence
May 27 2022

🔭 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.

NERData-centric AINLP
May 23 2022

🔭 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.

NLPML Data IntelligenceData-centric AI
May 03 2022

Introducing ML Data Intelligence For Unstructured Data

“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.

ML Data Intelligence

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