How to build a powerful MLOps platform with Red Hat OpenShift


June 23, 2020

 

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How to build a powerful MLOps platform with Red Hat OpenShift_short

 

 

MLOps & Red Hat OpenShift (preview)

Have you ever considered using Machine Learning and Artificial Intelligence to boost your business? I bet you have! Check this webinar to learn more about it.

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About the Webinar

Nowadays, many companies are using Machine Learning (ML) and Artificial Intelligence (AI) to boost their business. Some great examples of that are Netflix, Spotify and Amazon, and these solutions have revolutionized the world across all industries.

But while the potential of AI and ML is undeniable, organizations face challenges scaling AI. In trying to leverage these solutions, companies face major challenges like:

  • Sharing and Collaboration: Unable to easily share and collaborate, iteratively and rapidly 
  • Data access: Access to data is bespoke, manual and time consuming 
  • On-demand: No on-demand access to ML tools and frameworks and compute infrastructure
  • Production: Models are remaining prototypes and not going into production 
  • Tracking and Explaining AI: Reproducing, tracking and explaining results of AI/ML is hard


To try to solve these challenges, companies rely on their IT departments to deliver a cloud-like experience to data scientists: a platform that offers freedom of choice, easy access, fast and agile, scales on-demand while being resilient.

So how can you address this? By building an AI platform that is container based, supports AI/ML services with agile processes and is built with the hybrid cloud in mind.

This webinar is directed to Business Leaders, Data Architects, Data Engineers, Data Scientists and IT Operations teams, with the purpose of demonstrating how to build a powerful ML platform with Red Hat OpenShift.

tiago fonseca

Tiago Fonseca

Chief Commercial Officer @ Syone
Rui Violante

Rui Violante

SOLUTIONS MANAGER @ Syone
José Ángel de Bustos

José Ángel de Bustos

Solution Architect @ Red Hat
 

Agenda

  • Welcome & Introduction
  • AI and Machine Learning at an Enterprise Level
  • Data Science: Models
  • OpenShift for Data Science
  • Open Data Hub Demo
  • How to move forward
 

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