Terality makes Data Scientists more productive, enabling them to execute all their pandas code at light speed, whatever the volume, by just changing their import line.Contact us for a demo
From 30x faster at 1 GB - to 100x faster at 1 TB
Evertyhing happens securely on our infrastructure, not your local machine Never worry about memory again
Your pandas code runs up to 100 times faster than before. Less than a minute on 1 terabyte of data.
Use all the pandas functions, even the more complex ones such as sort, merge and groupby.
Keep the same syntax and get the same results, simply faster.
You can even use Terality on existing code.
Terality handles 100% of the infrastructure in the cloud, and auto-scales as needed.
Terality runs your code out of core on servers in the cloud. No local memory is used .
You can trust us with your data. Data is encrypted at rest and in transit with the latest security standards.
Scaling pandas is hard for everyone. We built Terality so that you can focus on your specific business problems.
Thanks to Terality, Data Scientists save tens of hours per month without having to change anything in their daily workflow.
Data Scientists are 100% autonomous from the engineering team to run their code, whatever the data volume. Machine Learning Engineers are free to focus on their core activities.
With Terality, companies meet their strategic objectives faster thanks to their Data Science projects.
MEET OUR CO-FOUNDERS
Guillaume has been working in tech companies for the past 6 years. He participated in the growth of Algolia from 200 to 8,500 customers.
He also worked with strategic accounts at Datadog.
Guillaume graduated in Business Management from EDHEC Business School, and Business Law from Aix-en-Provence Faculty of Law.
With 6 years of experience in Machine Learning, and three years as a Data Scientist, Adrien is the technical lead of the project, bringing his expertise in Data Science, Machine Learning and Software development.
Adrien graduated in applied mathematics from Ecole Polytechnique, and was a PhD student in Machine Learning at INRIA.