ML pipeline on Kubeflow: How to go from rapid prototyping to production

Topic details: Running a machine learning application in production, in a reliable and repeatable manner, is a challenge. This is because an end to end machine learning application entails a lot of phases in addition to model training: data pre-processing and validation, feature engineering, model analysis, deployment, et al. You need a system that makes it easy to compose, orchestrate and run such multi-step pipelines. However, wouldn't it be amazing if the same system also enabled rapid and reliable experimentation of ML techniques for your application. Come learn how Kubeflow enables and simplifies these dual goals.

Speaker Bio: Anand Iyer is a Product Manager at Google Cloud, focused on delivering industry leading machine learning solutions that delight users. He is particularly passionate about the intersection of data, machine learning and open source. Prior to Google, he gained experience building and delivering machine learning platforms at Cloudera and LinkedIn. He holds a masters in computer science from Stanford and a bachelors from the University of Arizona.

Agenda
5:45 - 6:30 PM Networking/Social/Food
6:30 - 6:35 PM Introduction
6:35 - 7:30 PM Talk and Q&A
7:30 - 8:00 PM Networking/Social/Closing