Google & ML - 6/26

Hi all,

To allow a deeper dive into the platform, the meetup will have two talks of ~45 min (and not 3 as usual).

Following is the bio and Abstract for the talks:
1.
Abstract:

Data is the lifeblood of machine learning. The ability to effectively manage and process data at scale is a critical component of doing machine learning in production. In this talk I will introduce some of the tools we have developed with Apache Beam and TensorFlow to do machine learning at scale for both preparing data and evaluating models. I will also demonstrate how Beam's model of pluggable backends allow one to run the same pipeline both locally and on distributed backends like Flink and Google Cloud Dataflow.

Bio:

Robert Bradshaw is a software engineer at Google, developing on tools for doing petabyte-scale data processing, most recently working on Apache Beam. He is also active in the open source community, leading the Cython project since it's inception and as a long-time contributor to the open source mathematics software Sage. He received Ph.D. in Mathematics from University of Washington and currently resides in Seattle, Washington.

2:
Abstract:
How to use the cloud machine learning infrastructure to prototype and develop machine learning based application that can scale up quickly to a word wide usage.
I will share Oriel Research (OR) process from data to a trained model. This experience could be applied to any other industry ML based solution that you might be working on. I will discuss high level guidelines that were implied for OR and code samples. The main tools that will be demonstrated are: Datalab, Dataflow, Apache-beam, TF.hub

Bio:
Eila Arich-Landkof
Entrepreneur with broad experience at the high-tech and life science industries. Worked for Cisco, Microsoft, Mass General Hospital, Whitehead institute for biomedical research and The Broad institute of Harvard and MIT.
Currently leading this meetup and Oriel Research startup that diagnoses disease based on the patients genomic & molecular information using machine learning methods.

Looking forward to seeing you.
Best,
Eila