Loading…
October 12, 2021
Los Angeles, California + Virtual
View More Details & Registration

The Sched app allows you to build your schedule but is not a substitute for your event registration. You must be registered for KubeCon + CloudNativeCon North America 2021 - Los Angeles, CA + Virtual and add this Co-Located event to your registration to participate in these sessions. If you have not registered but would like to join us, please go to the event registration page to purchase a registration.

Please note: This schedule is automatically displayed in Pacific Standard Time (PST), UTC -7. To see the schedule in your preferred timezone, please select from the drop-down menu to the right, above "Filter by Date." The schedule is subject to change.


IMPORTANT NOTE: Timing of sessions and room locations are subject to change through Monday, September 13 due to schedule changes that will be made as speakers finalize whether speaking in person or virtually.

Sign up or log in to bookmark your favorites and sync them to your phone or calendar.

Tuesday, October 12
 

9:00am PDT

Opening Remarks - Thiago Gil, 4 Intelligence
Join us as we kick off Kubernetes AI Day North America 2021!

Speakers
TG

Thiago Gil

Machine Learning Engineer, 4intelligence & FinOps Foundation Brazil Event Coordinator, 4 intelligence


Tuesday October 12, 2021 9:00am - 9:10am PDT
Room 502 AB + Online

9:10am PDT

Sponsored Keynote: Kubeflow Pipelines v2: The Next Generation of MLOps on Kubernetes - Karthik Ramachandran, Product Manager, Google Cloud Vertex AI, Google
We would like to introduce Kubeflow Pipelines v2, which makes it significantly easier to author and manage model training Pipelines. In this talk, we’ll review the new capabilities of the project, talk a little bit about our plans for the future, and discuss where and how the community can help.

Speakers
KR

Karthik Ramachandran

Product Manager, Google Cloud Vertex AI, Google


Tuesday October 12, 2021 9:10am - 9:20am PDT
Room 502 AB + Online

9:20am PDT

Sponsored Keynote: Evolving with Kubernetes: Embracing Model Ops - Steven Huels, Sr Director, AI Cloud Services, Red Hat
Kubernetes changed the way in which applications are developed. AI and Machine Learning has changed what is required of an application which has driven changes in the way we put intelligent applications into production. Applying the DevOps principles to AI and Machine Learning outputs or Model Ops, can help you get your models from pilot to production safely, securely, and with repeatability.

Speakers
avatar for Steven Huels

Steven Huels

Sr Director, AI Cloud Services, Red Hat
Steven Huels is a Director in the Red Hat AI Center of Excellence with responsibility for the Data Hub, the Common AI Library, Thoth, and AI Ops.


Tuesday October 12, 2021 9:20am - 9:30am PDT
Room 502 AB + Online

9:30am PDT

What, Why and How of Federated Machine Learning – Implementation Using FATE, KubeFATE, and FATE-Operator for Kubeflow - Neeraj Arora & Layne Peng, VMware
Federated Machine Learning (FML) is an emerging technology that makes it possible to extract insights from widely dispersed data sources. It may also be used with privacy enhancing measures to reveal insights without revealing the underlying data. In doing so, it gives global organizations an opportunity to utilize worldwide data while adhering to regulations for different geographic regions, allows insights to be developed without moving large amounts of data to a central location, and in a multi-party scenario allows each party to control their individual decision to participate. This talk will introduce FML discussing its basic principles, its manifestations, high-level use-cases, and tie these in with the technology being developed. To move from theory to practice, we will demonstrate how FML can be implemented on Kubernetes using FATE, KubeFATE, and integrated with Kubeflow using the FATE-Operator. We’ll also discuss new features being implemented into FATE for realistic use-cases. A similar presentation was delivered at CNOS Virtual Summit China in July 2020 and focused on the integration of FATE into Kubeflow. The current presentation will cover technical ground instead on the challenges and opportunities of FML for real-world use-cases encountered since.

Speakers
avatar for Layne Peng

Layne Peng

Staff II Technologist, VMware
avatar for Neeraj Arora

Neeraj Arora

Application Platforms Architect, VMware



Tuesday October 12, 2021 9:30am - 10:00am PDT
Room 502 AB + Online

10:00am PDT

Break
Break

Tuesday October 12, 2021 10:00am - 10:20am PDT

10:20am PDT

Security Best Practices for AI on Kubernetes - Guy Salton, Run:AI
Data Scientists and MLOps engineers are embracing containers and Kubernetes for building, debugging, training and deploying deep learning models. There are many advantages for using Kubernetes for AI workloads, but is it secure? In this talk, we will present the security concerns for AI workloads running on Kubernetes and how to mitigate them: Which user is used inside the container? Can the Data Scientist use privileged escalation from his container and access the host filesystem? How to allow Data Scientists to install python packages in a secure manner? Can a Data Scientist have access other researchers code and data from his container? Guy Salton, Solution Engineering Lead at Run:AI, will cover all the concerns above, and provide security best practices to MLOps engineers, to make the everyday work of Data Scientists both secure and productive.

Speakers
avatar for Guy Salton

Guy Salton

Solution Engineering Lead, Run:AI
Guy is a Solutions Architect specializing in the fields of DevOps, Cloud Computing, Kubernetes, Containers, CI/CD and AI computing.Running POCs and technical projects for commercial and enterprise customers, on-site installations and workshops.Guy shares his knowledge by talking in... Read More →



Tuesday October 12, 2021 10:20am - 10:50am PDT
Room 502 AB + Online

10:50am PDT

Making Complex R Forecast Applications Into Production Using Argo Workflow - Natalia Costa Araujo, Pedro Szloma Herr Zaterka & Matheus Sesso Gay, 4intelligence
R has tools to help multidisciplinary teams succeed. Usually, the processes developed in R are created for problems restricted to academics researchers. As more companies search for data and scientific methods to guide business decisions, creating a scalable R environment will be a critical step towards success.

Speakers
avatar for Pedro Szloma Herr Zaterka

Pedro Szloma Herr Zaterka

Machine Learning Engineer, 4intelligence
Pedro works as a Research Data Scientist and Machine Learning Engineer at 4intelligence, developing auto-ML solutions in time series forecasting and bringing models and pipelines to production.
avatar for Matheus Sesso Gay

Matheus Sesso Gay

Data Scientist, 4intelligence
Matheus works as a Research Data Scientist at 4intelligence, developing algorithms and coordinating the time series agenda, always focusing on automated solutions.
avatar for Natalia Costa Araujo

Natalia Costa Araujo

Data Scientist, 4intelligence
Natalia works as a Research Data Scientist at 4intelligence, developing auto-ML and statistical solutions in time series forecasting to support companies in data-driven decisions.



Tuesday October 12, 2021 10:50am - 11:20am PDT
Room 502 AB + Online

11:20am PDT

Case Study: Developing and Scaling Kubeflow’s Web Apps - Andrey Velichkevich, Cisco & Kimonas Sotirchos, Arrikto
At this moment, Kubeflow maintains at least 5 different web apps, for managing Notebooks, PVCs, Tensorboards, Models, AutoML Experiments, that allow users to interact with the platform. At their core these web apps act as a graphical interface for performing CRUD operations on top of K8s Objects and Custom Resources. Designing, creating, and maintaining these apps is not a trivial task. In this talk, attendees will learn how the Kubeflow community overcame all the challenges to create true cloud native web apps, for managing ML workflows on top of K8s. Follow our journey as we explore the architectural decisions we made regarding authentication with Istio and authorization with K8s SubjectAccessReviews. How we factored out the common code and enabled application scalability. The UX decisions for managing K8s objects via a GUI. And last but not least, how we can efficiently fetch new data for Kubeflow dashboard in the context of how users can perform advanced AutoML techniques.

Speakers
avatar for Kimonas Sotirchos

Kimonas Sotirchos

Software Engineer, Arrikto
Kimonas is a Software Engineer interested in cloud native applications and distributed systems. Loves to work on open source projects, collaborate and develop innovative software as part of a community. Has been a core Kubeflow contributor for more than two years with expertise around... Read More →
avatar for Andrey Velichkevich

Andrey Velichkevich

Senior Software Engineer, Cisco
Andrey Velichkevich is a Senior Software Engineer at Cisco and is a contributor to the Kubeflow open-source project. He is a co-chair for the AutoML working group and co-lead for the Training working group. Andrey hosts Kubeflow community meetings for the AutoML and Training working... Read More →



Tuesday October 12, 2021 11:20am - 11:50am PDT
Room 502 AB + Online

11:50am PDT

Defending Against Adversarial Model Attacks Using Kubeflow - Animesh Singh & Andrew Butler, IBM
The application of AI algorithms in domains such as self-driving cars, facial recognition, and hiring holds great promise. At the same time, it raises legitimate concerns about AI algorithms robustness against adversarial attacks. Widespread adoption of AI algorithms where the predictions are hidden or obscured from the trained eye of the subject expert, opportunities for a malicious actor to take advantage of the AI algorithms grow considerably, necessitating the addition of adversarial robustness training and checking.  To protect against and mitigate the damages caused by these malicious actors,  this talk will examine how to build a pipeline that’s robust against adversarial attacks by leveraging Kubeflow Pipelines and integration with LFAI Adversarial Robustness Toolbox (ART). Additionally we will show how to test a machine learning model's adversarial robustness in production on Kubeflow Serving, by virtue of Payload logging (KNative eventing) and ART.

Speakers
avatar for Animesh Singh

Animesh Singh

CTO and Distinguished Engineer, Watson AI/ ML Open Tech, IBM
IBM CTO for Watson AI/ML Open Technology. Watson Distinguished Engineer and Executive, responsible for Watson AI/ML Platform Open Technology strategy, architecture and execution, delivering joint IBM Watson and Red Hat technical roadmap and products, working with Watson customers... Read More →
avatar for Andrew Butler

Andrew Butler

Developer - Deep Learning/Machine Learning/AI Advocate, IBM
Andrew Butler is a Machine Learning Software Developer for IBM, where he works on incorporating tools that increase trust in machine learning models by looking at the explainability, robustness, and fairness of those models. In addition, he works on a project that provides Kubernetes-style... Read More →



Tuesday October 12, 2021 11:50am - 12:20pm PDT
Room 502 AB + Online

12:20pm PDT

Lunch
Tuesday October 12, 2021 12:20pm - 1:35pm PDT

1:35pm PDT

AIOps for CI with Kubeflow Pipelines - Oindrilla Chatterjee & Aakanksha Duggal, Red Hat
It’s easy to get lost in logs and dashboards while getting to the root of build or test failures. By leveraging the data made available by Kubernetes testing and visualization platforms like Prow and TestGrid, we have built AI4CI (Artificial Intelligence for Continuous Integration), an open source AIOps tool to monitor builds and help developers get to the root cause of failures. In this session, the speakers demonstrate how they developed a set of Jupyter Notebooks which are automated using Kubeflow Pipelines into a repeatable process that collects data from various CI/CD tools, calculates key performance indicator metrics, and performs analyses such as failure type prediction and build log clustering. These metrics are then displayed and explored as interactive dashboards. By the end of the talk, the audience will learn how to build AIOps monitoring tools and automated dashboards that can help provide more visibility into their tests to better support their CI/CD processes.

Speakers
avatar for Oindrilla Chatterjee

Oindrilla Chatterjee

Data Scientist, Red Hat
Oindrilla is a Data Scientist at Red Hat, working in the Office of the CTO working on emerging trends and research in ML and AI. She spent the past year developing open source AI applications for CI data.
avatar for Aakanksha Duggal

Aakanksha Duggal

Software Engineer, Red Hat
Aakanksha Duggal is a Software Engineer at Red Hat working in the AI Centre of Excellence and Office of the CTO. She is a part of the AIOps team and works in developing open source software that uses AI and machine learning applications to solve engineering problems.



Tuesday October 12, 2021 1:35pm - 2:05pm PDT
Room 502 AB + Online

2:05pm PDT

Serving Machine Learning Models at Scale Using KServe - Yuzhui Liu, Bloomberg
KServe (previously known as KFServing) is a serverless open source solution to serve machine learning models. With machine learning becoming more widely adopted in organizations, the trend is to deploy larger numbers of models. Plus, there is an increasing need to serve models using GPUs. As GPUs are expensive, engineers are seeking ways to serve multiple models with one GPU. The KServe community designed a Multi-Model Serving solution to scale the number of models that can be served in a Kubernetes cluster. By sharing the serving container that is enabled to host multiple models, Multi-Model Serving addresses three limitations that the current ‘one model, one service’ paradigm encounters: 1) Compute resources (including the cost for public cloud), 2) Maximum number of pods, 3) Maximum number of IP addresses. 4) Maximum number of services This talk will present the design of Multi-Model Serving, describe how to use it to serve models for different frameworks, and share benchmark stats that demonstrate its scalability.

Speakers
avatar for Yuzhui Liu

Yuzhui Liu

Senior Software Engineer, Bloomberg
Yuzhui Liu is a senior software engineer who works for the Data Science Platform team at Bloomberg. She is passionate about building infrastructure for machine learning applications. In her spare time, she likes to travel around the world and enjoy local food.


Tuesday October 12, 2021 2:05pm - 2:35pm PDT
Room 502 AB + Online

2:35pm PDT

Break
Tuesday October 12, 2021 2:35pm - 2:55pm PDT

2:55pm PDT

A Better and More Efficient ML Experience for CERN Users - Ricardo Rocha & Dejan Golubovic, CERN
Experiments at CERN such as the Large Hadron Collider (LHC) generate petabytes of new data every year, to be stored and analyzed by thousands of physicists around the world. In just a couple years, an upgrade to the LHC will trigger a 10x increase in the amount of data posing a challenge to the existing infrastructure. This session covers how machine learning has been gaining momentum in the high energy physics (HEP) community and particularly at CERN, as a viable option to handle the data growth with a similar amount of resources. The focus is on one particular service based on Kubeflow, and how we extend the existing functionality to offer our users a familiar and seamless integration with site services. How centralizing resources has improved our overall resource usage, how we extended existing functionality to manage end user tokens and credentials allowing access to on-premises storage, and how we explore tools like Harbor, Trivy, OPA and Falco to ensure a reproducible and secure flow from interactive analysis, to model training and finally serving.

Speakers
avatar for Ricardo Rocha

Ricardo Rocha

Computing Engineer, CERN
Ricardo is a Computing Engineer in the CERN cloud team focusing on containerized deployments, networking and more recently machine learning platforms. He has pushed for several years the internal effort to transition services and workloads to use cloud native technologies, as well... Read More →
avatar for Dejan Golubovic

Dejan Golubovic

Software Engineer, CERN
Dejan Golubovic is a CERN software engineer with experience in machine learning. His interests are containerized applications, Python programming and large-scale distributed systems. Dejan is currently working on machine learning infrastructure with Kubernetes and Kubeflow at CERN... Read More →



Tuesday October 12, 2021 2:55pm - 3:25pm PDT
Room 502 AB + Online

3:25pm PDT

Closing Remarks - Thiago Gil, 4 Intelligence
Join us for closing remarks and a wrap-up of the day's content!

Speakers
TG

Thiago Gil

Machine Learning Engineer, 4intelligence & FinOps Foundation Brazil Event Coordinator, 4 intelligence


Tuesday October 12, 2021 3:25pm - 3:35pm PDT
Room 502 AB + Online

5:00pm PDT

CNCF-hosted Co-located Events Happy Hour
Join us onsite for drinks and appetizers with fellow co-located attendees from Tuesday's CNCF-hosted Co-located Events. Network with attendees from:
  • Cloud Native DevX Day North America hosted by CNCF
  • Cloud Native Security Conference North America hosted by CNCF
  • Cloud Native Wasm Day North America hosted by CNCF
  • FluentCon North America hosted by CNCF
  • GitOpsCon North America hosted by CNCF
  • Kubernetes AI Day North America hosted by CNCF + LFAI & Data
  • ServiceMeshCon North America hosted by CNCF

Tuesday October 12, 2021 5:00pm - 6:30pm PDT
Los Angeles Convention Center, Petree Plaza
 
  • Timezone
  • Filter By Venue Los Angeles, California, USA
  • Filter By Type
  • Break
  • Breakout Sessions
  • Closing Remarks
  • Lunch
  • Networking
  • Opening Remarks
  • Sponsored Keynote
  • Content Experience Level
  • TALK TYPE