Making AI/ML Simple, Fast and Cost-effective with Google Kubernetes Engine (Cloud Next '19)

In this session, we will discuss how Google’s AI/ML building blocks are making AI/ML simple, fast and efficient for data scientists, data engineers, devops engineers and everyday users. We will explore how Kubernetes, Kubeflow and Kubeflow Pipelines can help to mitigate complexities and challenges associated with AI/ML. We will demonstrate the use of Accelerators like GPUs and TPU in Kubernetes Engine to make serving compute intensive ML/AI workloads easy, fast and scalable. We will present the real world examples of commonly used AI/ML applications, discuss their performance and share best practices. We will also present how the economics are different when it comes to ML workloads and highlight the unique values Kubernetes brings to enterprises. Watch more: Next '19 ML & AI Sessions here → https://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform Speaker(s): Maulin Patel, Murali Karumuri, Priyesh Wani, Mahadevan Balasubramaniam Session ID: HYB220 product: Cloud - Containers - Google Kubernetes Engine (GKE); fullname: Maulin Patel, Murali Karumuri, Priyesh Wani, Mahadevan Balasubramaniam; event: Google Cloud Next 2019;

Видео Making AI/ML Simple, Fast and Cost-effective with Google Kubernetes Engine (Cloud Next '19) автора PythonFocus