Deploying Deep Learning

 Most interesting  article...

Lessons From Deploying Deep Learning to Production

By The Gradient  May 18, 2022

Peter Gao is the co-founder and CEO of Aquarium, and he previously worked on machine learning for self-driving cars, education, and social media.

In an article, Peter Gao discusses his experiences deploying deep learning into production environments. "There are a lot more people using deep learning in production applications nowadays compared to when I was doing research at Berkeley, but many problems that they face are the same ones I grappled with in 2016 at Cruise," Gao writes.

"I used to think that machine learning was about the models. Actually, machine learning in production is about pipelines. One of the best predictors of success is the ability to effectively iterate on your model pipeline. That doesn't just mean iterating quickly, but also iterating intelligently...otherwise you end up with a pipeline that produces bad models very quickly."

"The vast majority of problems with model performance can be solved with data, but there are certain issues that can only be solved with changes to the model code." -Peter Gao

From The Gradient

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Individualizing Mass Production

Seems a powerful idea,   The data is all there, so to what degree and for what products can this be applied and scaled for manufacturing production?   Proposed details at the link.

Mass production of individualized products
Fraunhofer Research News / June 02, 2020

How can mass production methods be applied to individualized products? One answer is to use a combination of digital manufacturing technologies, for example by integrating digital printing and laser processing into traditional manufacturing processes. This paves the way for in-line product customization. Six Fraunhofer institutes have pooled their expertise to take the new process to the next level.

The term mass production generally suggests large numbers of identical products rolling off an assembly line. However, the latest trends call for individualized products. The automotive industry is one example for this trend: Volkswagen, for example, produces only one or two identical Golf models a year. Yet this drive toward individualization is also pushing mass production techniques to their limits. The Fraunhofer Lighthouse Project Go Beyond 4.0 aims to meet this challenge by enabling the mass production of individualized products. It is a collaboration between four different Fraunhofer Groups and six Fraunhofer Institutes: the Fraunhofer Institute for Electronic Nano Systems ENAS, the Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM, the Fraunhofer Institute for Laser Technology ILT, the Fraunhofer Institute for Applied Optics and Precision Engineering IOF, the Fraunhofer Institute for Silicate Research ISC and the Fraunhofer Institute for Machine Tools and Forming Technology IWU. The project is managed by Fraunhofer ENAS in Chemnitz.  ... " 

Embracing Responsible AI from Pilot to Production

A topic rarely addressed well, we discovered early on it had to be carefully done.  Upcoming DSC webinar:

Embracing Responsible AI from Pilot to Production
Join us for the latest DSC Webinar on May 27th, 2020
register-now: https://dsc.news/2LFMEHz

On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization. In this latest Data Science Central webinar, we'll talk about the framework for scaling AI pilots to production with a focus on ethical responsibilities and bias mitigation at each step.

We'll look at:

The five-step AI development cycle
Ways to control for unwanted bias across data, models, and run time at the production layer
Explainability and why it is key for moving AI pilots to production that delivers core business value

Speakers:
Lukas Biewald, Founder & CEO -- Weights & Biases
Alyssa Simpson-Rochwerger, VP of AI & Data -- Appen

Hosted by: Sean Welch, Host and Producer -- Data Science Central 
Title: Embracing Responsible AI from Pilot to Production
Date: Wednesday, May 27th, 2020
Time: 9 AM - 10 AM PDT 
Space is limited so please register early:
https://dsc.news/2LFMEHz

Reserve your Webinar seat now 

After registering you will receive a confirmation email containing information about joining the Webinar.  ... 

Talk: State of Production Machine Learning

Recently from the Linkedin Group: Cognitive Systems Institute.   https://www.linkedin.com/groups/6729452/   A talk that I missed and am now reviewing.  In particular my interest in how ML can be put in production, both in establishing from data,  testing, and renewing models.

Via Susan Malaika: ... Alejandro Saucedo Chief Scientist at the Institute for Ethical AI & Machine Learning & the Director of Machine Learning Engineering at Seldon Technologies on "The State of Production Machine Learning in 2020" ....

 ... The recording posted: https://youtube.com/watch?v=nuvRMFM8USE and the slides are here:  https://ethicalml.github.io/state-of-mlops-2020/#/ - Thank you Alejandro for the excellent overview on production ML 2020 - and the many resources you shared including AI Guidelines:  https://github.com/EthicalML/awesome-artificial-intelligence-guidelines  .... '