Date of event: August 28, 2019


8.00am - 9.00am

Check-in and Breakfast

9.00am - 9.30am

Welcome to Google Developers ML Summit '19

30 min • Kyle Paul, Google Developers

Kyle will deliver a welcome speech and talk briefly about Google Developers programs and campaigns.

9.30am - 10am

Learning the Depths of Moving People by Watching Frozen People

30 min • Forrester Cole

This talk will present recent work on predicting dense depth maps from video when the camera and people in the scene are freely moving. 


10am - 10.30am

Ease ML deployments with TensorFlow Serving

30 min • Hannes Hapke, AI+Engineering at Caravel

 In this talk, Hannes, Google Developer Expert, will share his experience with deploying TensorFlow Serving in a variety of setups and deep learning models. He will also provide a preview of the upcoming O'Reilly book "Managing and Building Machine Learning Workflows".


10.30am - 10.45am


10.45am - 11.15am

Lessons from the Inner Sanctum: Enter the bowels of password lists Rockyou and Pwned.

30 min • Anastasia Sagalovitch

The aim of this talk is to explore recently made available password lists utilizing natural language processing and machine learning techniques used in classifying and clustering big data problems. 

11.15am - 11.45am

The What-If Tool: Code-Free Probing of Machine Learning Models

30 min • Tolga Bolukbasi, Google Brain

Learn about the What-If Tool, an open-source, interactive visualization for probing ML models, useful for model understanding, debugging performance and fairness concerns, and comparing models.


11.45am - 1.00pm


1.00pm - 1.30pm

Lightning Talks

10 min • Wale Akinfaderin, Duke Energy

Using Natural Language Processing to Understand Parliamentary Bills in Low Resource Countries 

10 min • Ekaba Bisong, Google Developer Expert

Building a Language Toxicity Classification Model using Google Cloud AutoML 

10 min • Vikraman Karunanidhi, Ayu Devices

A Deep Neural Network to Identify Foreshocks in Real Time

1.30pm - 2.00pm

AI Workbench: Empowering Businesses to Realize Impact with AI

30 min • Hallie Crosby, Google ML Expert

This talk will walk through the suite of tools Google's AI Platform offers to enable your team to get up and running quickly to create custom machine learning models. 


2.00pm - 2.30pm

TensorFlow.js: Bringing Machine Learning to the Web and Beyond

30 min • Daniel Smilkov and Nikhil Thorat, Google Brain

In this talk, you will learn about the TensorFlow.js ecosystem: how to bring an existing ML model into your JS app and re-train the model using your data. We’ll also go over our efforts beyond the browser to bring ML to platforms such as React Native, Raspberry Pi, and Electron, and we’ll do a live demo of some of our favorite and unique applications!


2.30pm - 3.00pm

Art + AI : Generating Novel African Mask Art using Generative Adversarial Networks

30 min • Victor Dibia, Cloudera Fast Forward Labs

Victor will curate a dataset of 11000 images of African Mask art and train a Generative Adversarial Neural Network (GAN) that learns to generate new masks. 


3.00pm - 3.30pm

Protein function prediction by neural networks

30 min • David Belanger, Google Brain

Motivation: Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms.

Results: We report convolutional neural networks that are substantially more accurate and computationally efficient than current state of the art methods, like BLAST, while learning sequence features such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. 


3.30pm - 3.45pm


3.45pm - 4.15pm

Fusing AI + AR on Android

30 min • , Stephen Wylie, Google Developer Expert

This talk explains leveraging the basic MobileNet object detection tool in an Android app using Tensorflow Lite to discover where an object of interest is, and then use ARCore to render something around it in 3D space. Then, it will touch on how to train a model for your own needs of classifications, plus some of the pitfalls of trying to render an AR asset relative to arbitrary objects in space.


4:15pm - 4.45pm

Panel Discussion

30 min • , Forrester Cole, David Belanger, Hallie Crosby

Google Brain and Google Cloud experts will be on the panel to answer questions on Machine Learning and AI topics


4.45pm - 5.00pm

Closing Remarks

5.00pm - 6.30pm

Happy Hour and Networking