Articles
-
FEB 20, 2019
As most deep learning engineers know, it can take days or weeks to train a deep learning model, costing organizations considerable time and money. But what if we could speed up the process and achieve better results in the process?
-
JAN 29, 2019
2019 has gotten off to a good start for us at Determined AI. As a company focusing on accelerating deep learning model development for our users, we saw the deep learning community gathering together at the RE•WORK Deep Learning Summit and it further validated our mission.
-
DEC 18, 2018
Deep learning offers the promise of bypassing the process of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion.
-
OCT 16, 2018
In this post, we discuss how a technique known as warm-starting can be used to save computational resources and improve generalizability when training deep learning models.
-
AUG 30, 2018
Last week, we at Determined AI were honored to sponsor a meetup of the Women in Infrastructure group focused on ML infrastructure.
-
AUG 20, 2018
In this post, we discuss the missing key to fully leveraging your hardware investment: specialized software that understands the unique properties of deep learning workloads.
-
JUL 25, 2018
To maximize the value of your deep learning hardware, you’ll need to invest in software infrastructure. Setting up a cluster manager is an essential first step in this process, but it’s not the end of the story.
-
JUN 28, 2018
You’ve probably had some version of this debate numerous times: cloud or on-premise? Maybe you want to migrate some pieces of your application to the cloud.
-
MAY 25, 2018
Reproducing results across machine learning experiments is painstaking work, and in some cases, even impossible.
-
MAY 01, 2018
I recently spoke at the AI Conference in NYC about some of the academic research underlying our efforts at Determined AI.
-
APR 25, 2018
Machine learning today resembles the dawn of aviation.
-
MAR 15, 2018
I recently joined the O’Reilly Data Show podcast to talk about various challenges associated with developing deep learning at scale.