Christopher Cho demonstrates how Kubernetes can be easily leveraged to build a complete deep learning pipeline, including data ingestion and aggregation, preprocessing, ML training, and serving with the mighty Kubernetes APIs.

Vijay Reddy walks you through the process of building machine learning models with TensorFlow. You'll learn about data exploration, feature engineering, model creation, training, evaluation, deployment, and more.

IMPORTANT NOTE:Due to the nature of tutorials, this session has been placed in a smaller capacity room to help facilitate a conducive learning environment. Space is very limited and seating will be g …

Machine Learning is become an increasingly popular topic in the world of data. At the same time, the concept of microservices through containerization has increased in popularity as it allows for deve …

Turning ML into magical products often requires complex, distributed systems that bring with them a unique, ML specific set of infrastructure problems. A year ago, we started building Kubeflow to leve …

Schireson, a New York City based data science and engineering firm, overcame a challenge in how to properly scale kubernetes services for their application stack. Consuming data from several tools i …

How do you bootstrap an open source project that aims to provide stability, composability, and portability for machine learning? You use Kubernetes and its rich ecosystem to implement the pieces of in …

The Strata Data conference in London takes place during one of the most important weeks in the history of data regulation, as GDPR begins to be enforced. Steve Touw explores the effects of the GDPR on deploying machine learning models in the EU.

The Kubeflow project is aimed at simplifying developing, deploying and using ML on Kubernetes. We will provide an introduction to Kubeflow that explains the benefits of using Kubeflow and walk through …

Apache Spark is an amazing distributed system, but part of the bargain we've made with the infrastructure deamons involves providing the correct set of magic numbers (aka tuning) or our jobs may be eaten by Cthulhu. Holden Karau, Rachel Warren, and Anya Bida explore auto-tuning jobs using systems like Apache BEAM, Mahout, and internal Spark ML jobs as workloads.

Reza Zadeh details three challenges on the way to building cutting-edge ML products, with a focus on computer vision, offering examples, recommendations, and lessons learned.

Having trouble coordinating development of your production ML system between a team of developers? Microservices drifting and causing problems during debugging? Kinnary Jangla explains how Pinterest dockerized the services powering its home feed and how it impacted the engineering productivity of the company's ML teams while increasing uptime and ease of deployment.

ML researchers spend too much time building infrastructure to support their work. Kubeflow aims to solve that by using Kubernetes to build an open, scalable, and extensible platform for ML. Since our …

The Kubeflow project is aimed at simplifying developing, deploying and using ML on Kubernetes.This session will provide an in-depth understanding of Kubeflow. The talk will focus on the different com …

Acme Corporation is a global leader in commerce marketing. Manu Mukerji walks you through Acme Corporation's machine learning example for universal catalogs, explaining how the training and test sets are generated and annotated; how the model is pushed to production, automatically evaluated, and used; production issues that arise when applying ML at scale in production; lessons learned; and more.

Machine learning is a pivotal technology. However, bringing an ML application to life often requires overcoming bottlenecks not just in the model code but in operationalizing the end-to-end system itself. Goodman Gu shares a case study from a leading SaaS company that quickly and easily built, trained, optimized, and deployed an XGBoost churn prediction ML app at scale with Amazon SageMaker.

Power Poetry is the largest online platform for young poets, with over 350K users. Ann Nguyen explains how Power Poetry is extending the learning potential with machine learning and covers the technical elements of the Poetry Genome, a series of ML tools to analyze and break down similarity scores of the poems added to the site.

Open source machine learning tools to manipulate and store big data and apply complex algorithms to this data have matured greatly in recent years. However, the final step in any successful machine …