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.
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!
9:00am–5:00pm Tuesday, September 4, 2018
Location: Imperial A
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.
9:00am–5:00pm Wednesday, September 5, 2018
Location: Imperial A
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.
11:05am–11:45am Thursday, September 6, 2018
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.
11:55am–12:35pm Thursday, September 6, 2018
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.
1:45pm–2:25pm Thursday, September 6, 2018
2:35pm–3:15pm Thursday, September 6, 2018
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.
4:00pm–4:40pm Thursday, September 6, 2018
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.
4:50pm–5:30pm Thursday, September 6, 2018
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.
11:05am–11:45am Friday, September 7, 2018
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.
11:55am–12:35pm Friday, September 7, 2018
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.
1:45pm–2:25pm Friday, September 7, 2018
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.
2:35pm–3:15pm Friday, September 7, 2018
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.
4:00pm–4:40pm Friday, September 7, 2018
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.
4:50pm–5:30pm Friday, September 7, 2018
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.