Enabling support for data processing, data analytics, and machine learning workloads in Kubernetes has been one of the goals of the open source community. During this meetup we’ll discuss the growing use of Kubernetes for data science and machine learning workloads. We’ll examine how new Kubernetes extensibility features such as custom resources and custom controllers are used for applications and frameworks integration. Apache Spark 2.3.’s native support is the latest indication of this growing trend. We’ll demo a few examples of data science workloads running on Kubernetes clusters setup by our Kublr platform.
Oleg Chunikhin, CTO at Kublr, has been working in the field of software architecture and development for nearly 20 years. He joined Kublr in 2017, as he specializes in DevOps technologies including Kubernetes, Docker, and Puppet. Oleg has successfully defined Kublr’s technology strategy and innovative standards, tools, technologies, and processes.
Arkadii Ocheretno, Lead Platform Developer at Kublr, has been working with Kubernetes since 2016 and has broad experience with enterprise projects that depend on many different technologies, both on-premises and in the private cloud. Arkadii has also worked extensively on cloud-native environments and has comprehensive experience with Azure and AWS.