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In-person
November 12-15
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Friday November 15, 2024 11:55am - 12:30pm MST
In this talk, we will explore employing machine learning (ML) algorithms to enhance the Kubernetes autoscaling capabilities beyond the traditional, reactive horizontal pod autoscaler (HPA). Attendees will be introduced to how to leverage recommendation algorithms to predict future load and usage patterns, allowing for smarter, proactive scaling decisions. This approach not only ensures high availability and responsiveness of applications but also offers a pathway to substantial cost optimizations by preventing over-provisioning and minimizing resource wastage.
Speakers
avatar for Avni Sharma

Avni Sharma

Product Manager, Intuit
Avni is a Product Manager at Intuit, working on Intuit’s Modern SaaS Kubernetes platform. She also worked on ArgoCD as a PM. Avni is passionate about Developer tooling and strives to make developers' life easier by delivering them delightful experiences. She is also an Open Source... Read More →
avatar for Estela Ramirez

Estela Ramirez

Software Engineer, Intuit Kubernetes Service, Intuit
Estela is a Software Engineer at Intuit focusing on Intuit Kubernetes Developer Platform. She works on abstracting the autoscaling for developers.
Friday November 15, 2024 11:55am - 12:30pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

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