TimeGPT: The First Foundation Model for Time Series
Azul (She/Her) is a highly experienced ML engineer, with a background in economics and mathematics. She is the CTO and co-founder of Nixtla. With over a decade of expertise in deploying ML models in production for large financial institutions, Azul has a proven track record of delivering end-to-end products. She is passionate about creating usable, scalable, and open-source ML products, and is a co-maintainer of several popular Python libraries. Azul's expertise in the field has also earned them recognition as a speaker at multiple Pycons and author of peer-reviewed papers, solidifying their status as a leading expert in time series analysis.
Azul (She/Her) is a highly experienced ML engineer, with a background in economics and mathematics. She is the CTO and co-founder of Nixtla. With over a decade of expertise in deploying ML models in production for large financial institutions, Azul has a proven track record of delivering end-to-end products. She is passionate about creating usable, scalable, and open-source ML products, and is a co-maintainer of several popular Python libraries. Azul's expertise in the field has also earned them recognition as a speaker at multiple Pycons and author of peer-reviewed papers, solidifying their status as a leading expert in time series analysis.
I'm a tech entrepreneur and I spent the last decade founding companies that drive societal change.
I am now building Deep Matter, a startup still in stealth mode...
I was most recently building Telepath, the world's most developer-friendly machine learning platform. Throughout my previous projects, I had learned that building machine learning powered applications is hard - especially hard when you don't have a background in data science. I believe that this is choking innovation, especially in industries that can't support large data teams.
For example, I previously co-founded Call Time AI, where we used Artificial Intelligence to assemble and study the largest database of political contributions. The company powered progressive campaigns from school board to the Presidency. As of October, 2020, we helped Democrats raise tens of millions of dollars. In April of 2021, we sold Call Time to Political Data Inc.. Our success, in large part, is due to our ability to productionize machine learning.
I believe that knowledge is unbounded, and that everything that is not forbidden by laws of nature is achievable, given the right knowledge. This holds immense promise for the future of intelligence and therefore for the future of well-being. I believe that the process of mining knowledge should be done honestly and responsibly, and that wielding it should be done with care. I co-founded Telepath to give more tools to more people to access more knowledge.
I'm fascinated by the relationship between technology, science and history. I graduated from UC Berkeley with degrees in Astrophysics and Classics and have published several papers on those topics. I was previously a researcher at the Getty Villa where I wrote about Ancient Greek math and at the Weizmann Institute, where I researched supernovae.
I currently live in New York City. I enjoy advising startups, thinking about how they can make for an excellent vehicle for addressing the Israeli-Palestinian conflict, and hearing from random folks who stumble on my LinkedIn profile. Reach out, friend!
I'm a tech entrepreneur and I spent the last decade founding companies that drive societal change.
I am now building Deep Matter, a startup still in stealth mode...
I was most recently building Telepath, the world's most developer-friendly machine learning platform. Throughout my previous projects, I had learned that building machine learning powered applications is hard - especially hard when you don't have a background in data science. I believe that this is choking innovation, especially in industries that can't support large data teams.
For example, I previously co-founded Call Time AI, where we used Artificial Intelligence to assemble and study the largest database of political contributions. The company powered progressive campaigns from school board to the Presidency. As of October, 2020, we helped Democrats raise tens of millions of dollars. In April of 2021, we sold Call Time to Political Data Inc.. Our success, in large part, is due to our ability to productionize machine learning.
I believe that knowledge is unbounded, and that everything that is not forbidden by laws of nature is achievable, given the right knowledge. This holds immense promise for the future of intelligence and therefore for the future of well-being. I believe that the process of mining knowledge should be done honestly and responsibly, and that wielding it should be done with care. I co-founded Telepath to give more tools to more people to access more knowledge.
I'm fascinated by the relationship between technology, science and history. I graduated from UC Berkeley with degrees in Astrophysics and Classics and have published several papers on those topics. I was previously a researcher at the Getty Villa where I wrote about Ancient Greek math and at the Weizmann Institute, where I researched supernovae.
I currently live in New York City. I enjoy advising startups, thinking about how they can make for an excellent vehicle for addressing the Israeli-Palestinian conflict, and hearing from random folks who stumble on my LinkedIn profile. Reach out, friend!
Time series—data ordered chronologically—constitutes the underlying fabric of systems, enterprises, and institutions. Its impact spans from measuring ocean tides to tracking the daily closing value of the Dow Jones. This type of data representation is indispensable in sectors such as finance, healthcare, meteorology, and social sciences. However, the current theoretical and practical understanding of time series hasn't yet achieved a consensus among practitioners that mirrors the widespread acclaim for generative models in other fundamental domains of the human condition, like language and perception. Our field is still divided and highly specialized. Efforts in forecasting science have fallen short of fulfilling the promises of genuinely universal pre-trained models. In this talk, we will introduce TimeGPT, the first pre-trained foundation model for time series forecasting that can produce accurate predictions across various domains and applications without additional training. A general pre-trained model constitutes a groundbreaking innovation that opens the path to a new paradigm for the forecasting practice that is more accessible and accurate, less time-consuming, and drastically reduces computational complexity. We will show how to use TimeGPT in a live demo.