July 13–16, 2020

Build end-to-end, production-grade, scalable AI apps on Kubernetes

Karthic Rao (Dgraph Labs)
10:00am–5:30pm Monday, 07/13/2020
Location: E143/144

To attend, participants must be registered for a Training Pass. Please note: 2-Day Training passholders have access to TWO 1-day training courses, ONE on Monday and ONE on Tuesday. 1-Day Training passholders have access to ONE 1-day training course on Monday OR Tuesday.

Building an end-to-end, production-grade machine learning application is complex and involves tens of components. Karthic Rao demystifies this process as he walks you through building a Google Photos replica on Kubeflow—an open source project for building ML pipelines leveraging the capabilities offered by 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.

Level

Intermediate

Prerequisites:

  • 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

Photo of Karthic Rao

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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