TensorFlow World 2019 — keynotes

Last week I had a chance to join TensorFlow World 2019 organized in Santa Clara. It was three days full of knowledge and inspiring talks. I prepared a subjective summary of the most interesting topics discussed at the conference.


Google announced long term support for TensorFlow on Google Cloud Platform called TensorFlow Enterprise. It will contain:

  • Enterprise-grade support — up to 3 years of TensorFlow versions support and white-glove support, including engineer-to-engineer assistance by Google Cloud and TensorFlow teams.
  • Cloud scale performance — a wide selection of hardware options to train and deploy ML models (including CPU, GPU, and TPU).

Kubeflow Summit —Google Campus, Sunnyvale, October 2019

Last week I had a pleasure to be a speaker on the Kubeflow Summit organized on Google Campus in Sunnyvale, CA. It was a unique experience to meet community around 100 Kubeflow users and contributors, listen to exciting talks, and to share my and team’s expertise on moving ML pipelines to Google Cloud Platform.

Open a high-resolution image (Updated: 20190807)

What is Kubeflow? It’s an open-source project integrating the most popular data science tools in one place. The main concept is to streamline build machine learning applications — from the prototype to the production. With Kubernetes under the hood, it’s an exciting toolkit that can make Data Scientists and Data Engineers life easier. This article is a subjective introduction to Kubeflow from a data scientist perspective.

Kubeflow components out-of-the-box

Once you have a running Kubeflow instance, you will get integrated components that allow you to build machine learning models and put them into production.


Notebooks became an…

Michal Brys

Data Scientist, Google Cloud Certified Professional Data Engineer https://www.linkedin.com/in/michalbrys

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