Past Meetup

Kubernetes: Machine Learning, AI, and Running it all on Kubernetes

This Meetup is past

300 people went

Location image of event venue

Details

Kubernauts! We are excited to announce the next San Francisco Kubernetes meetup will be December 13, 2016 at Chartboost in San Francisco.

Also, if you'd like to present please ping me via meetup or twitter (@baldwinmathew (https://twitter.com/baldwinmathew)).

SPONSORED: Spin up and manage a Kubernetes cluster with Istio (https://stackpoint.io/clusters/new?solution=istio&utm_source=meetup&utm_medium=event_page&utm_campaign=sf_k8s) at AWS, GCE, GKE, DigitalOcean or Azure today!

Agenda:

6:30 - 7:00 - Social

7:00 - 7:30 - Building Google's ML Engine from Scratch with GPUs, Kubernetes, Istio, and TensorFlow - Chris Fregly

Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow.

In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.

This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.

Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production.

Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.

Learn to be fast and strong by attending this meetup.

7:30 - 8:00 - Machine Learning Pipelines on Kubernetes - Anirudh Ramanathan

Kubernetes as an application deployment platform can help set up and deploy machine learning applications, all the way from training to production. This talk describes the newly evolving ML stack built entirely on Kubernetes. It also goes into details of how one can use a combination of different tools to create a portable and powerful ML stack.

8:00 - Social, Wrap-up, Jet

Bio/Briefs(s)

Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production." Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.

Twitter: @cfregly (https://twitter.com/cfregly)

Anirudh Ramanathan is a software engineer on the Kubernetes team at Google. He has contributed to several core features in Kubernetes such as StatefulSets, Pod Disruption Budget, etc. He currently leads the BigData efforts under SIG Big Data with a focus on running batch workloads. As part of the SIG, he has worked on native Kubernetes support within Spark, Airflow, Tensorflow, JupyterHub and other applications. Prior to this, he worked on GGC (Google Global Cache) and before that, on the infrastructure team at NVIDIA.

Location/Instructions:

Our friends at Chartboost have offered to provide space for this upcoming event.

The address is:

Chartboost

85 2nd Street

Suite 100

San Francisco, CA 94105