Arnaud Hubaux's guest appearances

Arnaud Hubaux

Google Cloud Platform Podcast

IpX True North Podcast

Dave Elliott

Andrew Walker

Peter Norvig

Eric Anderson

John Bohannon

Manuel Lima

Margaret Mitchell

Jessie Frazelle

Joanna Smith

Juliet Hougland

Michelle Casbon

Jesse Houston

Mark Chmarny

Ville Aikas

Dale Markowitz

OwlTail

In this second part episode of the True North Podcast, IpX Director of MBE, Max Gravel and Senior Technical Program Manager, ASML, Arnaud Hubaux continue their discussion on the challenges of maintaining the physics model in AI/ML with rapidly changing datasets. Arnaud describes the need for an enterprise-level AI/ML framework that considers the complete lifecycle change to expand beyond niche use-cases and realize the dream of a fully automated product line. Max and Arnaud discuss where to start when implementing AI and the role of CM in maturing the adoption of AI in a manufacturing environment. Hear more on:The need for the impact analysis to stretch beyond the walls of the company into the environment where it will be running.The perceived unpredictability of AI/ML and the reluctance to deploy in a manufacturing environment.The importance of building in safety mechanisms to prevent financial loss in case of AI/ML failure.How CM establishes gates and checks when releasing a change to make sure the proper qualifications are implemented.AI/ML sustainability and the need to look beyond short-term benefits into the complete lifecycle of a product.Connect with IpX to hear from more industry thought leaders and to learn about the True North service model for improving your organization. https://ipxhq.com/

In this second part episode of the True North Podcast, IpX Director of MBE, Max Gravel and Senior Technical Program Manager, <a href="https://www.asml.com/en" rel="nofollow" target="_blank">ASML</a>, Arnaud Hubaux continue their discussion on the challenges of maintaining the physics model in AI/ML with rapidly changing datasets.  Arnaud describes the need for an enterprise-level AI/ML framework that considers the complete lifecycle change to expand beyond niche use-cases and realize the dream of a fully automated product line. Max and Arnaud discuss where to start when implementing AI and the role of CM in maturing the adoption of AI in a manufacturing environment. Hear more on:<ul><li>The need for the impact analysis to stretch beyond the walls of the company into the environment where it will be running.</li><li>The perceived unpredictability of AI/ML and the reluctance to deploy in a manufacturing environment.</li><li>The importance of building in safety mechanisms to prevent financial loss in case of AI/ML failure.</li><li>How CM establishes gates and checks when releasing a change to make sure the proper qualifications are implemented.</li><li>AI/ML sustainability and the need to look beyond short-term benefits into the complete lifecycle of a product.</li></ul>Connect with IpX to hear from more industry thought leaders and to learn about the True North service model for improving your organization. <a href="http://www.ipxhq.com" rel="nofollow" target="_blank">https://ipxhq.com/</a>

Pt 2: Maintaining the Physics Model within AI & ML with Arnaud Hubaux, ASML

In this episode of the True North Podcast, IpX Director of MBE, Max Gravel and Senior Technical Program Manager, ASML, Arnaud Hubaux discuss the challenges of maintaining the physics model in AI/ML with rapidly changing datasets. Arnaud gives a detailed review of how ASML uses AI to deliver greater value through new products for their customers and to accelerate manufacturing processes by qualifying high performing parts. Max and Arnaud discuss ASML’s unique approach to machine learning solutions with customers and how AI optimizes production and performance. Hear Arnaud break down:The challenges and necessity of explainability in AI/ML — where a causal clarification is needed between the observed effects and what is really happening physically in order to improve future designEnsuring continual guaranteed performance upon installation and after deployment of their machines to manage drift and ensure dataset accuracy The challenge and criticality of safety for people and materials first Defining and maintaining baselines consisting of thousands of parameters between both design and production Understanding the change impact to physics models: how to perform validation and verifications activities on the physics models before changes are applied to the customer product in order to mitigate risks with machine behavior.Connect with IpX to hear from more industry thought leaders and to learn about the True North service model for improving your organization. https://ipxhq.com/

In this episode of the True North Podcast, IpX Director of MBE, Max Gravel and Senior Technical Program Manager, <a href="https://www.asml.com/en" rel="nofollow" target="_blank">ASML</a>, Arnaud Hubaux discuss the challenges of maintaining the physics model in AI/ML with rapidly changing datasets.  Arnaud gives a detailed review of how ASML uses AI to deliver greater value through new products for their customers and to accelerate manufacturing processes by qualifying high performing parts. Max and Arnaud discuss ASML’s unique approach to machine learning solutions with customers and how AI optimizes production and performance. Hear Arnaud break down:<ul><li>The challenges and necessity of explainability in AI/ML — where a causal clarification is needed between the observed effects and what is really happening physically in order to improve future design</li><li>Ensuring continual guaranteed performance upon installation and after deployment of their machines to manage drift and ensure dataset accuracy </li><li>The challenge and criticality of safety for people and materials first</li><li> Defining and maintaining baselines consisting of thousands of parameters between both design and production</li><li> Understanding the change impact to physics models: how to perform validation and verifications activities on the physics models before changes are applied to the customer product in order to mitigate risks with machine behavior.</li></ul>Connect with IpX to hear from more industry thought leaders and to learn about the True North service model for improving your organization. <a href="http://www.ipxhq.com" rel="nofollow" target="_blank">https://ipxhq.com/</a>

Pt 1: Maintaining the Physics Model within AI & ML with Arnaud Hubaux, ASML

ML in machinery is our main topic this week as Mark Mirchandani and Brian Dorsey talk with Arnaud Hubaux, development lead for ASML. Our good friend, fellow Googler, and ML specialist Dale Markowitz joins the conversation as well! With a potent combination of physics and Machine Learning, ASML develops machines that build the chips powering our essential devices. These highly refined machines optimize production for each specific customer, detect defects, and make corrections quickly and accurately by harnessing the power of Machine Learning. The machines also effectively train themselves to ensure the intense accuracy required is sustained over time. Arnaud describes the process these machines go through in order to accomplish these goals, including how they build and train their ML models with the help of physics. Arnaud tells us how the machine’s closed ML system works from zero to train the particular job it will do. By taking a minimum spec chip that has already been created, the machine learns the process for creating that chip and continues to improve that process. These machines are not only able to detect problems but can figure out why these issues are occurring, decreasing production downtime. Recently, ASML moved to Google Cloud. We talk about the products they use, why they chose Google Cloud, and their journey to the cloud. Arnaud Hubaux Arnaud Hubaux is development lead for Artificial Intelligence and Machine Learning products at ASML. He works with the world’s biggest chip manufacturers on AI-driven solutions to solve problems like optimizing production yield. As a trailblazer, Arnaud and his team implemented the first ASML AI application development pipeline on Google Cloud. With a PhD on applied constraint solving, Arnaud has deep experience as an architect of software platforms and product lifecycle management. Cool things of the week  Cloud migration: What you need to know (and where to find it)  blog All together now: Fleet-wide monitoring for your Compute Engine VMs  blog SRE Classroom: exercises for non-abstract large systems design  blog gVisor: Protecting GKE and serverless users in the real world  blog  Interview  ASML site ASML in One Minute video Zoom in on the chip in your smartphone video KubeFlow site BigQuery site AI Platform Notebooks site Cloud Build site Introduction to Kubeflow video Intro to Kubeflow Pipelines video How to Build a Kubeflow Pipeline video  Tip of the week We have a super cool tip on combining AI and WTH with meetings and webcams! Check out the Level Up - AI Director and Level Up - Real-Time Video Translation with AR Subtitles videos! What’s something cool you’re working on? Mark is working on some upcoming Kubernetes content. Brian is working on an upcoming series of GCE videos with Carter Morgan.

ML in machinery is our main topic this week as <a href="https://twitter.com/markmirch" rel="nofollow" target="_blank">Mark Mirchandani</a> and <a href="https://twitter.com/briandorsey" rel="nofollow" target="_blank">Brian Dorsey</a> talk with Arnaud Hubaux, development lead for ASML. Our good friend, fellow Googler, and ML specialist <a href="https://twitter.com/dalequark" rel="nofollow" target="_blank">Dale Markowitz</a> joins the conversation as well! With a potent combination of physics and Machine Learning, ASML develops machines that build the chips powering our essential devices. These highly refined machines optimize production for each specific customer, detect defects, and make corrections quickly and accurately by harnessing the power of Machine Learning. The machines also effectively train themselves to ensure the intense accuracy required is sustained over time. Arnaud describes the process these machines go through in order to accomplish these goals, including how they build and train their ML models with the help of physics. Arnaud tells us how the machine’s closed ML system works from zero to train the particular job it will do. By taking a minimum spec chip that has already been created, the machine learns the process for creating that chip and continues to improve that process. These machines are not only able to detect problems but can figure out why these issues are occurring, decreasing production downtime. Recently, ASML moved to Google Cloud. We talk about the products they use, why they chose Google Cloud, and their journey to the cloud. Arnaud Hubaux Arnaud Hubaux is development lead for Artificial Intelligence and Machine Learning products at ASML. He works with the world’s biggest chip manufacturers on AI-driven solutions to solve problems like optimizing production yield. As a trailblazer, Arnaud and his team implemented the first ASML AI application development pipeline on Google Cloud. With a PhD on applied constraint solving, Arnaud has deep experience as an architect of software platforms and product lifecycle management. Cool things of the week <ul> <li>Cloud migration: What you need to know (and where to find it) <a href="https://cloud.google.com/blog/products/cloud-migration/guide-to-all-google-cloud-migration-guides" rel="nofollow" target="_blank"> blog</a></li> <li>All together now: Fleet-wide monitoring for your Compute Engine VMs <a href="https://cloud.google.com/blog/products/management-tools/cloud-monitoring-gets-fleet-wide-vm-monitoring" rel="nofollow" target="_blank"> blog</a></li> <li>SRE Classroom: exercises for non-abstract large systems design <a href="https://cloud.google.com/blog/products/devops-sre/join-sre-classroom-nalsd-workshops" rel="nofollow" target="_blank"> blog</a></li> <li>gVisor: Protecting GKE and serverless users in the real world <a href="https://cloud.google.com/blog/products/containers-kubernetes/how-gvisor-protects-google-cloud-services-from-cve-2020-14386" rel="nofollow" target="_blank"> blog</a></li> </ul> Interview <ul> <li>ASML <a href="https://www.asml.com/en" rel="nofollow" target="_blank">site</a></li> <li>ASML in One Minute <a href="https://www.youtube.com/watch?v=wI6nCmG-PpI" rel="nofollow" target="_blank">video</a></li> <li>Zoom in on the chip in your smartphone <a href="https://youtu.be/2z9qme_ygRI" rel="nofollow" target="_blank">video</a></li> <li>KubeFlow <a href="https://www.kubeflow.org" rel="nofollow" target="_blank">site</a></li> <li>BigQuery <a href="https://cloud.google.com/bigquery" rel="nofollow" target="_blank">site</a></li> <li>AI Platform Notebooks <a href="https://cloud.google.com/ai-platform-notebooks" rel="nofollow" target="_blank">site</a></li> <li>Cloud Build <a href="https://cloud.google.com/cloud-build" rel="nofollow" target="_blank">site</a></li> <li>Introduction to Kubeflow <a href="https://www.youtube.com/watch?v=cTZArDgbIWw&amp;t=49s" rel="nofollow" target="_blank">video</a></li> <li>Intro to Kubeflow Pipelines <a href="https://www.youtube.com/watch?v=_AY8mmbR1o4" rel="nofollow" target="_blank">video</a></li> <li>How to Build a Kubeflow Pipeline <a href="https://www.youtube.com/watch?v=JY7za08sAIU" rel="nofollow" target="_blank">video</a></li> </ul> Tip of the week We have a super cool tip on combining AI and WTH with meetings and webcams! Check out the <a href="https://www.youtube.com/watch?v=jIyM_qT9RZw" rel="nofollow" target="_blank">Level Up - AI Director</a> and <a href="https://www.youtube.com/watch?v=DvZRm-cqE7s" rel="nofollow" target="_blank">Level Up - Real-Time Video Translation with AR Subtitles</a> videos! What’s something cool you’re working on? Mark is working on some upcoming Kubernetes content. Brian is working on an upcoming series of GCE videos with Carter Morgan.