Posts in: Blog

OCT 29, 2019

The squeeze on AI talent could cripple America’s most important companies

By Evan Sparks

With the AI revolution solidly underway, tech’s top 5 companies are investing huge amounts of money into AI development and AI engineering talent.

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AUG 19, 2019

Specialized AI chips hold both promise and peril for developers

By Evan Sparks

In the next few years, chipmaking giants and well-funded startups will race to gain market share.

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AUG 13, 2019

[Product feature series] One-click access to TensorBoard for model development and experimentation

By Desmond Chan

Training a massive deep neural network can be daunting. Many deep learning (DL) engineers rely on TensorBoard for visualization so that they can better understand, debug, and optimize their model code.

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JUN 04, 2019

The cloud giants have an AI problem

By Evan Sparks

The general perception of cloud computing is that it makes all compute tasks cheaper and easier to manage.

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MAY 20, 2019

Stop doing iterative model development

By Yoav Zimmerman

Imagine a world in which gradient descent or second-order methods have not yet been invented, and the only way to train machine learning models is to tune their weights by hand.

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MAR 05, 2019

Random Search is a hard baseline to beat for Neural Architecture Search

By Ameet Talwalkar

In a previous post on “What’s the deal with Neural Architecture Search?”, Liam Li and I discussed Neural Architecture Search (NAS) as a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures.

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FEB 20, 2019

Addressing the challenges of massively parallel hyperparameter optimization

By Ameet Talwalkar, Desmond Chan

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?

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DEC 18, 2018

What’s the deal with Neural Architecture Search?

By Liam Li, Ameet Talwalkar

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.

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OCT 16, 2018

Warm starting for efficient deep learning resource utilization

By Jennifer Villa, Yoav Zimmerman

In this post, we discuss how warm-starting can save computational resources and improve generalizability when training deep learning models.

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AUG 30, 2018

Recapping our first Women in Infrastructure meetup

By Jennifer Villa

Last week, we at Determined AI were honored to sponsor a meetup of the Women in Infrastructure group focused on ML infrastructure.

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