Date: April 27, 2023
Time: 16:00 UTC
Machine Learning (ML) is rapidly evolving and becoming essential to all businesses and organizations around the world. Data Scientists and Machine learning engineers looking to scale their AI workloads are faced with the challenges of handling large-scale AI in a distributed environment.
In this session, Avishay Sebban will give an overview of the challenges of running distributed workloads for machine learning. He’ll discuss the key advantages Kubernetes offers as a platform for training and deploying ML models, and eliminating the infrastructure complexity associated with scaling and managing containerized applications. You will learn how to utilize kubernetes for distributed workloads to easily scale your ML models and automate workload performance management, using cnvrg.io MLOps platform, KubeFlow and Ray.
Avishay Sebban is an experienced AI and Big Data professional with several years of experience in the field. Previously, he worked as a DevOps Engineer, where he gained valuable experience in software development and cloud native technologies.
As a former Ex Senior Solution Architect at Red Hat, Avishay specialized in designing and implementing cutting-edge solutions for clients in various industries. Currently, Avishay is part of the cnvrg.io, an Intel company, where he focuses on making cnvrg.io solutions excellent by working closely with clients to design, productize, and implement AI solutions that align with their business objectives.
Throughout his career, Avishay has gained a deep understanding of AI and Big Data technologies, and has a passion for staying up-to-date with the latest industry trends. Avishay is dedicated to leveraging his expertise to help clients achieve their goals and drive innovation in the field.