Webinar - Unpacking The State of Data Quality in Machine Learning

Overview

Raise your hand if you’ve been personally victimized by bad data quality. ✋

Yeah- us too. Undoubtedly data is the lifeblood of ML- but it just feels unfair that 80% of data scientists' time is spent on the most time-consuming, least enjoyable data science task- fixing data.

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.

Explore the challenges that lie ahead across data modalities, how technology can help, and the implications for the ML industry.

Sign up to hear insights on:

• How ML practitioners navigate through the challenges of finding the right data to train on
• Technological solutions that the industry has adopted to improve data quality across data modalities
• Role of technology in resolving data errors and monitoring production model drift.

Reserve Your Seat

Data Modalities Featured

Natural Language Processing

Computer Vision (Images)

Computer Vision (Video)

Unstructured Data

Speech Data

What is Galileo?

We are a Machine Learning Data Quality Intelligence Tool.

Using Galileo you can inspect and fix data quality errors in all stages of the ML process. Our team at Galileo had first-hand experience working on large-scale Machine learning projects at Uber Michaelangelo and Google Ai. It’s no wonder they turned their experience into an obsession to fix the data quality issues.

Where does Galileo fit in the ML Workflow?

• Pretraining- Find and fix data errors before training without a model.
• Training- Connect Galileo in your training model to resolve data errors.
• Production - monitor and resolve data drift errors.

At our core, we believe that together we can create a better, more productive, bias-free future for the world by focusing on high-quality data.

Use Galileo to save time and focus on far more enjoyable and challenging tasks- get started in our free community offering.

Join our slack to share your thoughts on this data quality hurdle.