Build end-to-end, production-grade, scalable AI apps on Kubernetes
What you'll learn, and how you can apply it
- Learn to build an end-to-end machine learning application
Who is this presentation for?
- You're a DevOps engineer, backend engineer, or data scientist who wants to get started with machine learning on Kubernetes.
- A basic understanding of containers and Kubernetes
Hardware and/or installation requirements:
- A laptop with 8 GB RAM and Docker installed
Machine learning pipelines comprise data cleaning, ML training, serving, and much more. This complexity poses severe challenges for deployment, scalability, performance, and portability of your machine learning pipeline and applications.
Kubeflow can help. Along with the best parts of Kubernetes like portability, ease of deployment, management, and scaling, Kubeflow simplifies the development and deployment of machine learning applications. Join Karthic to unlock the power of AI and learn to leverage it in your application.
About your instructor
Karthic Rao is a developer advocate at Dgraph Labs by day and a musician by evening. He has over five years of contributions to developing infrastructure tools in open source Go and has the unique distinction of contributing to a web server in Go (Caddy Server), a distributed object storage in Go (MinIO), and a distributed database in Go. Karthic is an avid speaker and writer; he’s presented talks across the globe and has over 275+ tech blog posts ing his arsenal. He’s also a Google Developer Expert in machine learning and a contributor to Kubeflow, an open source project by Google that aims to simplify machine learning on Kubernetes. Karthic organizes local meetups in Bangalore, including the GraphQL Bangalore Meetup and the CNCF’s Kubernetes India Data Management Meetups. When he’s not at work, you can find him spending time with family, at the gym, practicing music, or meditating.
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