Red Hat OpenShift / AI / Machine Learning | OpenShift Commons

OpenShift Commons Briefing
Continuous Development &Deployment of AI/ML Models with containers and Kubernetes
Guest Speakers:
Will Benton (Red Hat)
Parag Dave (Red Hat)
Peter Brey (Red Hat)
hosted by Diane Mueller (Red Hat)
2020-06-04

OpenDataHub Fraud Detection
ML SIG
OpenShift Commons
July 12 2019

Open Data Hub project is a reference architecture for an AI and Machine Learning as a service platform for OpenShift built using open source tools.

No description provided.

Diane Mueller - co-chair
Introductions
AIOps SIG announced
Drew Minter (UBIXLabs) – UBIX Platform Intro

Guest Speaker: Karl Wehden : VP of Product Strategy, Lightbend
Delivering business transformation at scale: How Lightbend and OpenShift can transform business by integrating business rules management into stream based systems. By combining Red Hat Decision Manager and Stream processing using Lightbend Pipelines, a new abstraction to simplify the development of streams processing, businesses can deliver clear decisions on time and with accuracy. By simplifying data delivery, decision processing, and doing these at scale, true change is possible. We’ll be highlighting the use of this technology in a financial services risk management example.
What you will see:
– Lightbend Platform on Red Hat OpenShift, and the capabilities of stateful services
– Safe and simple deployment of stream based processing of financial transactions using a globally consistent model
– The power of Red Hat Decision Manager at scale
– The resilience and strength of Cloud Native streams applications
About Lightbend:
Lightbend is the company behind open source application frameworks Akka, Play & Lagom and the Scala programming language. Lightbend Platform builds on these open source projects to create an application development platform and runtime for building modern, microservices-based distributed and real-time streaming applications.
Additional Resources:
https://www.lightbend.com/blog/how-to-deploy-kubeflow-on-lightbend-platform-openshift-installing-kubeflow
lightbend.com/blog
https://www.lightbend.com/blog/machine-learning-at-speed-operationalizing-ml-for-real-time-data-streams

OpenShift Commons Gathering Santa Clara 2019
Deep Learning Inference with Nvidia GPUs on OpenShift
Production Deep Learning Inference on Nvidia GPUs
Tripti Singhal (Nvidia)
Peter MacKinnon (RedHat)

OpenShift Commons Briefing
ML SIG March 2019 Full
MLFlow Intro and Operator
Mani Parkhe (Databricks)
Zak Hassan (Red Hat)
Diane Mueller (Red Hat)

OpenShift Commons
ML SIG Meeting
March 1 2019 Full Meeting Recording
MLFlow Introduction and MLFlow Operator demo
Databricks Mani Parkhe
Red Hat Zak Hassan and Diane Mueller

Deep Learning on OpenShift with GPUs | Tripti Singhal (Nvidia) | Tushar Katarki (Red Hat)

at OpenShift Commons Gathering Seattle 2018
https://commons.openshift.org/gatherings/Seattle_2018.html

Machine Learning on OpenShift SIG Using Ceph for ML Workloads on OpenShift - Kyle Bader (Red Hat)

Spark Operators
Using Ceph for ML workloads on OpenShift

Laval University Case Study
Guest Speaker: Guillaume Moutier (Laval University)
Data Science Services (Valeria)

Jeremy Wei and Brian Huang of Jeremy Wei (Prophetstor) – Demonstration of predicting computing resources usage for both nodes and containers, as well as predicting hardware issues.

"Data Hub" is a collection of open source and cloud components deployed as a "machine learning-as-a-service" platform to solve internal business problems at Red Hat that enables teams to build, deploy, and execute analytic, machine learning and AI models.

Repeatable human tasks are being replaced by automation, creating significant opportunity and risk for Red Hat. AI can be applied to our core business and direct customer services. To do so, data must be seamlessly unified from a broad range of sources and made accessible to analytic models.

This presentation is about how Red Hat runs AI and machine learning workloads on OpenShift.

With Artificial Intelligence and Machine Learning workloads expected to drive demand for the next wave of applications in the data center, Graphics Processing Unit (GPU) deployments using containers within an enterprise Linux environment will become more common. You will want to attend this session if you are a system administrator, developer or just looking to better understand how Red Hat is partnering with Nvidia to improve the developer experience, support containerized applications, and enable GPU and vGPU accelerated workloads to run on Red Hat Enterprise Linux for both bare-metal and hybrid cloud environments.

from the Machine Learning on OpenShift SIG meeting held on June 1 2018

from the OpenShift Commons Machine Learning on OpenShift SIG meeting June 1 2018

Alessandro Conconi of Intesa San Paolo presents their Machine Learning and OpenShift Case Study as part of OpenShift Commons Gathering Red Hat Summit 2018 What's Next on OpenShift Panel - moderated by Brian Gracely (Red Hat)

Carol Willing (Project Juytper) presentation of JuypterHub, BinderHub, repo2docker and how they all work on Kubernetes and on OpenShift to the Machine Learning on OpenShift SIG of OpenShift Commons

Will Benton (Red Hat OpenShift) demoing how to Operationalize Image Detection on OpenShift with Tensorflow to the Machine Learning on OpenShift SIG of OpenShift Commons

Subin Modeel and Will Benton (Red Hat OpenShift) demo Kubeflow on OpenShift to the Machine Learning on OpenShift SIG of OpenShift Commons

David Aronchick (Google) gives an introduction to Kubeflow to the Machine Learning on OpenShift SIG of OpenShift Commons.

Lachlan Evenson (Microsoft) discusses the results of his team's research on Distributed Training Performance on Kubernetes with the Machine Learning on OpenShift SIG of OpenShift Commons.

Learn more at https://github.com/joyq-github/TensorFlowonK8s

Join OpenShift Commons https://commons.openshift.org#join and join the conversation

Daniel Whitenack (Pachyderm) discusses how to enable Machine Learning and AI Data Pipelines on Kubernetes and OpenShift with the Machine Learning on OpenShift SIG of OpenShift Commons.

Learn more at http://docs.pachyderm.io/en/latest/getting_started/getting_started.html

Join OpenShift Commons https://commons.openshift.org#join and join the conversation

Michael Hausenblas (Red Hat OpenShift) discusses KAML-D (Kubernetes Advanced Machine Learning & Data Engineering Platform) open source project with the Machine Learning on OpenShift SIG of OpenShift Commons. Learn more about https://github.com/kaml-d/design

OpenShift Commons Gathering December 5th 2017 Austin, Texas
What's Next? Machine Learning on OpenShift Panel
Panelists: Matthew Farrellee (Red Hat), David Aronchick (Google), Kris Overholt (Anaconda)
Tushar Katarki, Red Hat, Moderator