Learn How To Build Machine Learning Models Faster at KubeCon Boston

KubeCon Boston may be virtual but we’re still here to help you experiment, train, and serve your models faster than ever before. Learn how to use Kubeflow to simplify and accelerate your machine learning projects!

Sessions

Machine Learning on Kubernetes at Shell: A Kubeflow Journey

 Tutorial: From Notebook to Kubeflow Pipelines to KFServing: the Data Science Odyssey

Owned by Statistics: Using Kubeflow to Defend vs Attacks on Your ML Models

Get All The Slides

Oculus Quest 2
Win Your Very Own Oculus Quest 2!

Want to win me?

Simply attend one or more of our KubeCon sessions and answer the online quiz on Twitter or LinkedIn.

Look for #kubecon #ml #kubeflow @arrikto
and follow the links.

 

Plenty of swag available too!

Machine Learning on Kubernetes at Shell: A Kubeflow Journey

When

Central European Time (CET / GMT+1)

November 18th – 21:50 – 22:25

US Eastern Time (EST / GMT-5)

November 18th – 15:50 – 16:25

US Pacific Time (PST / GMT-8)

November 18th – 12:50 – 13:25

How To Attend

In your browser, click the link below

https://sched.co/ekBt

Session Details

In this session, Shell describes the lessons learned from:

  • Working with multiple Machine Learning platforms and tools
  • The challenges of different systems, why we chose Kubeflow
  • How we are now delivering successful models faster and at scale.

Follow our journey as we learned how to deploy highly available, scalable, and secure Kubeflow clusters in the public cloud.

We will describe the lessons learned and steps taken to improve our deployments including enterprise authentication and authorization, network integration, and data science workflows.

We also discuss why we moved away from other platforms and chose Kubeflow, and how it has increased our Data Scientists’ productivity and reduced DevOps overhead.

Today our teams are more self-sufficient, and iterate faster to produce production-ready models in a timely fashion. A zero to hero story made possible by Kubeflow and Kubernetes

Speakers

Alex Iankoulski

Alex Iankoulski

Technical Leader, ML Orchestration @ Shell New Energies

Alex Iankoulski is a full stack software and infrastructure architect who likes to stay hands-on. He is a Docker Captain who has been helping accelerate the pace of innovation by applying container technologies to solve engineering, data science, AI and ML problems. Alex is currently Technical Leader, ML Orchestration at Shell New Energies where he supports Shell’s ambition to reduce the global carbon footprint. He works to bridge the gap between prototype and product using K8s and Kubeflow. His prior experiences include a tenure at GE Global Research, BHGE, as well as roles in the Healthcare, Network Security and Telecom industries. Alex has presented at conferences like DockerCon, Microsoft Azure AI Conf, Google Next, and is very excited to be a first-time speaker at KubeCon 2020.

Vangelis Koukis

Vangelis Koukis

CTO

Vangelis Koukis is the CTO and founder of Arrikto. Vangelis holds a PhD in computer science and has a long history of working in storage, data management, and cloud computing. Arrikto is a core contributor to the Kubeflow project, mainly in the areas of data management and UX. Arrikto creates software to transform how distributed applications discover and consume data on-prem or on the cloud, and empowers end users to iterate faster and easier, creating new collaboration workflows among teams.

From Notebook to Kubeflow Pipelines to KFServing: the Data Science Odyssey

When

Central European Time (CET / GMT+1)

November 20th – 21:10 – 22:35

US Eastern Time (EST / GMT-5)

November 20th – 15:10 – 16:35

US Pacific Time (PST / GMT-8)

November 20th – 12:10 – 13:35

How To Attend

In your browser, click the link below

https://sched.co/ekFQ

Session Details

A hands-on lab-based tutorial to show Data Scientists and ML Engineers alike how to turbocharge your Kubeflow efforts.

In this session, you will learn how to quickly build, tune, and execute complex Kubeflow workflows – as well as how to work faster using Kale to automate much of your work.

Learn how to rapidly automate Kubeflow:

  • Deploy a Jupyter Notebook as a Kubeflow pipeline using Kale – Optimize your model training using Katib for hyperparameter tuning
  • Serve your model with KFServing – Run thousands of runs with caching and garbage collection
  • Track and reproduce pipeline steps along with their state and artifacts Data Scientists benefit from an intuitive GUI that automates and hides all of the underlying infrastructure and SDK requirements.

ML Engineers can use the reproducible, automated workflows as a scaffold to quickly move to even more advanced tuning and model building.

This talk is a lab tutorial you can follow

Speakers

Karl Weinmeister

Karl Weinmeister

Cloud AI Advocacy Manager @ Google

Karl Weinmeister is a Cloud AI Advocacy Manager at Google, where he leads a team of data science experts who develop content and engage with communities worldwide. Karl has worked extensively in machine learning and cloud technologies. He was a contributor to one of the first AI-based crossword puzzle solvers that are still referenced today.

Stefano Fioravanzo

Stefano Fioravanzo

Software Engineer

Stefano Fioravanzo is a Software Engineer at Arrikto, his background is in Data Science and ML Research. He understands the value of building robust Machine Learning infrastructure and providing Data Scientist with the necessary tools to scale up their workflows. He works as a full-time contributor to Kubeflow and he is the creator of the Kubeflow Kale project which enables Jupyter Notebooks deployments to Kubeflow Pipelines.

Owned by Statistics: Using Kubeflow to Defend vs Attacks on Your ML Models

When

Central European Time (CET / GMT+1)

November 18th – 23:45 – 00:20

US Eastern Time (EST / GMT-5)

November 18th – 17:45 – 18:20

US Pacific Time (PST / GMT-8)

November 18th – 14:45 – 15:20

How To Attend

In your browser, click the link below

https://sched.co/ekBz

Session Details

Machine learning continues its spread across the tech world and is now in use by more than 80% of enterprises world wide.

However, with the increased reliance on this technology, the spectre of additional security attack surface areas rises up. Machine learning attacks are a new area of opportunity for adversaries, and require a new way to approach defense.

In this talk, we will cover several of the most common ML attacks today and how to defend against them. We will also show how to use a sophisticated, cloud-native pipeline with Kubeflow will to enable organizations to detect, remediate and defend against future attacks.

Speakers

David Aronchick

David Aronchick

Head of OSS ML Strategy @ Microsoft

David leads Open Source Machine Learning Strategy at Azure. This means he spends most of my time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project.

Yannis Zarkadas

Yannis Zarkadas

Software Engineer

Yannis is a software engineer at Arrikto, working with Kubeflow and the Kubernetes sig-storage group. He loves contributing to open source projects and has authored the Cassandra Operator in Rook and the official Scylla Operator, which he is currently maintaining.

Want The Presentations?