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Posts tagged Data Quality
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.
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.
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.
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.
Working with Natural Language Processing?
Read about Galileo’s NLP Studio
