Architecting Scalable MLOps Solutions on Google Kubernetes Engine
We will dive into the architecture of an AI/ML platform built on GKE, highlighting the importance of portability, flexibility, scalability, and cost efficiency. The session will also cover the latest advancements in GPU and TPU integration, and how these technologies can accelerate AI workloads. Real-world use cases, ML training and ML serving, will illustrate the practical applications and benefits of GKE in managing AI/ML workloads at scale.
Enes is a Google Certified Professional Cloud Architect who specializes in cloud and platform engineering, Kubernetes, Google Cloud, Anthos, and Apigee consulting in the region with a Google Cloud Premier Partnership and Application Modernization Specialization. He has assisted multiple organizations in implementing DevOps practices and improving the reliability and scalability of their applications on Kubernetes. He also has experience in designing and implementing cloud infrastructure, and a deep understanding of best practices for cloud security, compliance, and governance. Enes has a strong passion for sharing his knowledge with others and helping them to be successful with DevOps, Kubernetes, and Cloud Architecture. He is organizer at Cloud Native Istanbul, instructor at Udemy and EC-Council.