Leveraging AWS and Kubernetes for Scalable AI/ML Workloads in the Cloud

This session explores advanced strategies for deploying and optimizing scalable AI/ML workloads using AWS services and Kubernetes. Participants will gain insights into implementing infrastructure as code (IaC), container orchestration with Amazon EKS, and integrating monitoring solutions to enhance system reliability. The presentation emphasizes real-world applications of AWS SageMaker and Kubernetes to build resilient pipelines for machine learning workflows.
Who is this presentation for?
This session is tailored for cloud engineers, DevOps professionals, data scientists, and IT decision-makers involved in managing scalable cloud-based AI/ML workloads.
Prerequisite knowledge:
Attendees should have a foundational understanding of cloud computing, containerization, and basic knowledge of machine learning workflows.
What you'll learn?
How to design and implement scalable, cost-effective AI/ML workflows using AWS and Kubernetes. Best practices for automating infrastructure deployment and monitoring AI/ML systems. Techniques for optimizing machine learning pipelines with AWS SageMaker, EKS, and CI/CD tools.

As an AWS Cloud Support Engineer with Amazon Web Services since 2018, my primary role involves assessing client infrastructure, identifying suitable technology solutions, and designing cost-effective cloud architectures. I influence purchasing decisions by ensuring alignment with business needs, optimizing resource allocation, and leveraging best practices for performance, scalability, and security.