Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Sponsored by:
Google Cloud

TensorFlow at the AI Conference

Google Cloud Platform and the TensorFlow team present four learning-packed days spotlighting TensorFlow and machine learning at the AI Conference in San Francisco. All conference attendees are welcome, including those with Pavilion Plus passes. To join us, please register here.

Overview

Tuesday: Serverless Machine Learning with TensorFlow on GCP

A day-long introduction to designing and building machine learning models on Google Cloud Platform. Read more.

Wednesday: End-to-End Machine Learning with TensorFlow on GCP

Spend the day working through the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction. Read more.

Thursday & Friday: TensorFlow Sessions

Two days of conference sessions covering major TensorFlow topics, such as distributed training, TF Lite, TensorFlow.js, and more, accompanied by a focus on ML offerings from Google Cloud, such as AutoML, CMLE, TPUs, and Kubeflow.

We invite you to join us and dive deeper into TensorFlow!

Schedule

9:00am-5:00pm Tuesday, September 4, 2018
Serverless machine learning with TensorFlow on GCP Day (sponsored by Google Cloud)
Location: Imperial A
Carl Osipov (Google)
Average rating: *****
(5.00, 2 ratings)
Carl Osipov leads an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you'll learn machine learning (ML) and TensorFlow concepts and develop skills in developing, evaluating, and productionizing ML models. Read more.
9:00am-5:00pm Wednesday, September 5, 2018
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud)
Location: Imperial A
Drew Hodun (Google Cloud)
Average rating: ****.
(4.00, 5 ratings)
In this day-long presentation, you'll walk through the process of building a complete machine learning pipeline, from ingest and exploration to training, evaluation, deployment, and prediction. Read more.
11:05am-11:45am Thursday, September 6, 2018
Building AI with TensorFlow: An Overview (sponsored by Google)
Location: Continental 6
Laurence Moroney (Google), Edd Wilder-James (Google), Sandeep Gupta (Google)
Average rating: *****
(5.00, 2 ratings)
TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Laurence Moroney, Edd Wilder-James, and Sandeep Gupta share major recent developments and highlight some future directions. Join in to learn how you can become more involved in the TensorFlow community. Read more.
11:55am-12:35pm Thursday, September 6, 2018
TensorFlow: Machine learning for programmers (sponsored by Google)
Location: Continental 6
Laurence Moroney (Google)
Average rating: *****
(5.00, 1 rating)
Laurence Moroney dives into machine learning, AI, deep learning, and more and explains where they fit in the programmers toolkit. Along the way, he walks you through what it's all about, cutting through the hype to show the opportunities that are available in machine learning. Read more.
1:45pm-2:25pm Thursday, September 6, 2018
TensorFlow for JavaScript (sponsored by Google)
Location: Continental 6
Nick Kreeger (Google), Ping Yu (Google)
Average rating: ****.
(4.00, 1 rating)
TensorFlow.js is the recently released JavaScript version of TensorFlow that runs in the browser and Node.js. Nick Kreeger and Ping Yu offer an overview of the TensorFlow.js ML framework and demonstrate how to perform the complete machine learning workflow, including training, client-side deployment, and transfer learning. Read more.
2:35pm-3:15pm Thursday, September 6, 2018
Swift for TensorFlow and TensorFlow Lite (sponsored by Google)
Location: Continental 6
Richard Wei (Google), Andrew Selle (Google)
Average rating: ****.
(4.00, 2 ratings)
Richard Wei and Andrew Selle discuss Swift for TensorFlow and TensorFlow Lite, covering the current status of development and the latest developments. They then teach you how to prepare your model for mobile and how to write code that executes it on a variety of different platforms. Read more.
4:00pm-4:40pm Thursday, September 6, 2018
TensorFlow Extended: An end-to-end machine learning platform for TensorFlow (sponsored by Google)
Location: Continental 6
Clemens Mewald (Google)
Average rating: ****.
(4.00, 1 rating)
As machine learning evolves from experimentation to serving production workloads, so does the need to effectively manage the end-to-end training and production workflow including model management, versioning, and serving. Clemens Mewald offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet. Read more.
4:50pm-5:30pm Thursday, September 6, 2018
Kubeflow: Portable machine learning on Kubernetes (sponsored by Google)
Location: Continental 6
Michelle Casbon (Google)
Average rating: *****
(5.00, 1 rating)
Michelle Casbon offers an overview of Kubeflow. By providing a platform that reduces variability between services and environments, Kubeflow enables applications that are more robust and resilient, resulting in less downtime, quality issues, and customer impact. It also supports the use of specialized hardware such as GPUs, which can reduce operational costs and improve model performance. Read more.
11:05am-11:45am Friday, September 7, 2018
TensorFlow, deep learning, and modern convolutional neural nets, without a PhD (sponsored by Google)
Location: Continental 6
Martin Görner (Google)
Average rating: *****
(5.00, 2 ratings)
Martin Görner explores the newest developments in image recognition and convolutional neural network architectures and shares tips, engineering best practices, and pointers to apply these techniques in your projects. No PhD required. Read more.
11:55am-12:35pm Friday, September 7, 2018
Cloud AutoML: Customize machine learning models with your own data (sponsored by Google)
Location: Continental 6
Torry Yang (Google)
Cloud AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google’s state-of-the-art transfer learning and neural architecture search technology. Torry Yang explores the AutoML Vision, Translate, and Natural Language services and APIs and demonstrates how powerful and easy they are to use. Read more.
1:45pm-2:25pm Friday, September 7, 2018
Ready, set, go: Using TensorFlow to prototype, train, and productionalize your models (sponsored by Google)
Location: Continental 6
Karmel Allison (Google)
Average rating: *****
(5.00, 1 rating)
Building machine learning models is a multistage process. TensorFlow's high-level APIs make this process smooth and easy, whether you're starting small or going big. Karmel Allison walks you through a practical example of building, training, and debugging a model and then exporting it for serving using these APIs. Read more.
2:35pm-3:15pm Friday, September 7, 2018
AutoGraph and Cloud TPUs (sponsored by Google)
Location: Continental 6
Alexandre Passos (Google), Frank Chen (Google )
Average rating: ****.
(4.00, 1 rating)
Alexandre Passos and Frank Chen offer an overview of TensorFlow AutoGraph, which automatically converts plain Python code into the TensorFlow equivalent, using source code transformation. They then lead a technical deep dive into Google's Cloud TPU accelerators and show you how to program them. Read more.
4:00pm-4:40pm Friday, September 7, 2018
Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google)
Location: Continental 6
Joshua Dillon (Google Research), Wahid Bhimji (NERSC)
Average rating: *****
(5.00, 1 rating)
Join in for two talks on TensorFlow in space and mathematics. Josh Dillon discusses TensorFlow Probablity (TFP), and Wahid Bhimji discusses deep learning for fundamental sciences using high-performance computing. Read more.
4:50pm-5:30pm Friday, September 7, 2018
Tensor2Tensor (sponsored by Google)
Location: Continental 6
Lucasz Kaiser (Google)
Average rating: ***..
(3.00, 1 rating)
Lucasz Kaiser offers an overview of Tensor2Tensor, a library of deep learning models and datasets that facilitates the creation of state-of-the art models for a wide variety of ML applications, such as translation, parsing, image captioning, and more, enabling the exploration of various ideas much faster than previously possible. Read more.