Put open source to work
July 16–17, 2018: Training & Tutorials
July 18–19, 2018: Conference
Portland, OR

Tensorflow conference sessions

11:30am–12:00pm Tuesday, 07/17/2018
Ton Ngo (IBM), Yi-Hong Wang (IBM)
We typically interact with a web app by clicking and typing information. However, there are situations when this interaction is not convenient or possible. Voice is a much more common and convenient method for interaction. Ton Ngo and Yi-Hong Wang explain how TensorFlow.js can help a web-based electronic health record system leverage deep learning models to make this voice interface possible.
2:30pm–3:00pm Tuesday, 07/17/2018
Fabio Buso (Logical Clocks AB)
Fabio Buso offers demonstrations of frameworks for building distributed TensorFlow applications on the Hops platform and walks you through the whole model lifecycle, from debugging and visualizing models on TensorBoard to parallel experimentation and distributed training (with the help of Spark) to model deployment and inferencing using TensorFlow Serving and Kubernetes.
1:45pm–2:25pm Thursday, 07/19/2018
Matt Ellis (TIBCO Software), Rei Kurokawa (Hitachi High-Tech Solutions)
By the year 2020, the world will have an estimated 20 billion IoT devices. Storing, processing, reasoning with, and extracting business value out of this data will require huge computational and financial resources. Matt Ellis and Rei Kurokawa share an approach that uses TensorFlow and Project Flogo to make predictions directly on edge devices without depending on cloud computing.
11:00am–11:30am Tuesday, 07/17/2018
Gunhan Gulsoy (Google Brain)
Gunhan Gulsoy shares the inside story of how the very popular source project TensorFlow is kept running and sheds light on how TensorFlow is continuously built and tested and how everything is kept green through dozens of changes daily.
9:00am–12:30pm Monday, 07/16/2018
Joshua Gordon (Google)
Josh Gordon leads a friendly introduction to deep learning, covering computer vision, natural language processing, and structured data classification. You'll learn how to use TensorFlow—the world’s most popular open source machine learning library—preview the latest APIs, explore best practices, and discover the resources that will help you continue learning.
1:30pm–2:00pm Tuesday, 07/17/2018
Hannes Hapke (SAP ConcurLabs)
Developing deep learning models with TensorFlow is often only half of the story. To be useful to the public, the model needs to be deployed. Hannes Hapke explains how to deploy your TensorFlow model easily with TensorFlow Serving, introduces an emerging project called Kubeflow, and highlights some deployment pitfalls like model versioning and deployment flow.
10:15am–10:30am Tuesday, 07/17/2018
Edd Wilder-James (Google)
The hacking room runs parallel to the sessions. Stop by to listen to table leaders describe the topics for their tables and decide which you want to visit.
4:50pm–5:00pm Tuesday, 07/17/2018
Edd Wilder-James (Google)
Listen in as hacking room participants share what they've achieved during the day. Then Edd Wilder-James closes TensorFlow Day.
3:30pm–4:00pm Tuesday, 07/17/2018
Data scientists and model developers routinely trade off data size or model complexity in order to fit within limited GPU memory resources. Scott Soutter and Jason Furmanek discuss IBM's updates to TensorFlow, which dramatically increase memory and model size. This technique, which is being upstreamed to the open source community, provides the ability to load the entire model in system memory.
11:50am–12:30pm Wednesday, 07/18/2018
Joseph Gregorio (Google)
Your continuous integration process produces torrents of data. Joseph Gregorio explains how to mine that data to drive improvements in your development process and offers an overview of Skia—an open source 2D graphics library that provides common APIs that work across a variety of hardware and software platforms.
9:00am–5:00pm Monday, 07/16/2018
Rich Ott (The Pragmatic Institute)
Incorporating machine learning capabilities into software or apps is quickly becoming a necessity. Rich Ott leads you through two days of intensive learning that include a review of linear algebra essential to machine learning, an introduction to TensorFlow, and a dive into neural networks.
9:00am–5:00pm Tuesday, 07/17/2018
Rich Ott (The Pragmatic Institute)
Incorporating machine learning capabilities into software or apps is quickly becoming a necessity. Rich Ott leads you through two days of intensive learning that includes a review of linear algebra essential to machine learning, an introduction to TensorFlow, and a dive into neural networks.
1:30pm–5:00pm Tuesday, 07/17/2018
Jon Manning (Secret Lab), Tim Nugent (Lonely Coffee), Paris Buttfield-Addison (Secret Lab)
Join Jonathon Manning, Tim Nugent, and Paris Buttfield-Addison to get up to speed with the new machine learning features of iOS and learn how to apply the Vision and Core ML frameworks to solve practical problems in object detection, face recognition, and more. These frameworks run on-device, so they work quickly with no network access, making them cost effective and user-privacy conscious.
2:00pm–2:30pm Tuesday, 07/17/2018
Paige Bailey (Microsoft)
Machine learning offers a powerful toolkit for building complex predictive systems. These models can provide immense business value and are often deployed in high-consequence environments, but it can be extremely dangerous to think of those quick wins as coming for free. Paige Bailey explains what happens when your data changes over time and fresh models must be produced continuously.
9:00am–9:05am Tuesday, 07/17/2018
Edd Wilder-James (Google)
Edd Wilder-James opens TensorFlow Day.
4:30pm–4:50pm Tuesday, 07/17/2018
alex kari (Camas Liberty Middle School), Al Kari (Manceps)
Alex Kari and Al Kari walk you through the code to download a Pokémon images dataset, train and freeze a TensorFlow model on Google Colaboratory, and compile and deploy it on the Google AIY Vision kit (which runs TensorFlow on a Raspberry Pi) to identify and provide stats on any Pokémon with its camera.
2:35pm–3:15pm Wednesday, 07/18/2018
Holden Karau (Independent)
TensorFlow is all kinds of fancy, from helping startups raising their series A in Silicon Valley to detecting if something is a cat. Holden Karau details how to use TensorFlow in conjunction with Apache Spark, Flink, and Beam to create a full machine learning pipeline.
4:00pm–4:30pm Tuesday, 07/17/2018
R has a rich history as an open source statistical computing project and is a mainstay of data science. Gabriela de Queiroz and Augustina Ragwitz explain how R has gotten together with TensorFlow to provide a great toolkit for deep learning.
4:15pm–4:55pm Wednesday, 07/18/2018
Rustem Feyzkhanov (Instrumental)
This year TensorFlow 1.4 was released. Rustem Feyzkhanov explains how he ported it to AWS Lambda and built an image recognition tool. The tool is cheaper than almost any alternatives and very scalable (a thousand functions can be run in parallel), and it integrates into cloud infrastructure.
9:00am–5:00pm Tuesday, 07/17/2018
The TensorFlow Community Day brings together TensorFlow contributors and users to share experiences, increase collaboration, and advance the state of open source machine learning.
9:05am–9:40am Tuesday, 07/17/2018
Sandeep Gupta (Google)
TensorFlow is one of the world's biggest open source projects, and it continues to grow in adoption and functionality. Sandeep Gupta shares major recent developments and highlights some future directions for the project.