Schedule: Sponsored sessions

Benoit Dherin 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.

TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Sandeep Gupta and Edd Wilder-James share major recent developments, highlight some future directions, and explain how you can become more involved in the TensorFlow community. Read more.

Building machine learning models is a multistage process. TensorFlow's high-level APIs make this process smooth and easy, whether you are starting small or going big. Amit Patankar walks you through building, training, and debugging a model and then exporting it for serving using these APIs. Read more.

Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Julien Simon offers a quick overview of SageMaker. Then, using Jupyter notebooks, he dives into the more advanced features of this service. Read more.

Ryan Sepassi offers an overview of Tensor2Tensor, an open source library of datasets and models and a framework for training, evaluation, and decoding, built on top of TensorFlow. Tensor2Tensor is actively used and maintained by scientists and engineers within Google Brain. Read more.

Joshua Dillon and Wolff Dobson discuss core TensorFlow Probability (TFP) abstractions and demo some of TFP's modeling power and convenience. They also share some of the recent results from Project Magenta, a research project exploring the role of machine learning in the process of creating art and music. Read more.

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, leveraging Google’s state-of-the-art transfer learning and neural architecture search technology. Lucio Floretta demonstrates the power and ease of use of AutoML Vision, Translate, and Natural Language. Read more.

Zack Akil shares pragmatic techniques and useful tools that can help you avoid common pitfalls when building ML, including tools for notebook collaboration and version control that will help prevent you and your teammates from stepping on each others' toes as well as an iterative ML model development approach that will prevent your project from stagnating. Read more.

TensorFlow.js is the recently released JavaScript version of TensorFlow that runs in the browser and Node.js. Daniel Smilkov and Nikhil Thorat offer an overview of the TensorFlow.js ML framework and share a demo of a complete machine learning workflow, including training, client-side deployment, and transfer learning. Read more.

TensorFlow AutoGraph automatically converts plain Python code into its TensorFlow equivalent, using source code transformation. Brian Lee and Priya Gupta demonstrate how to distribute your training in TensorFlow easily across multiple accelerators and machines. Read more.

Training complex machine learning models with large amounts of data can take a very long time. Thomas Norrie explores methods for accelerating this process by distributing training across multiple accelerators and machines and leads a technical deep dive into Google Cloud’s TPU accelerators. Read more.

Pete Warden discusses the surprising effectiveness of deep learning on low-power devices. Read more.

Whether you’re new to machine learning (ML) or you’re already an expert, Google Cloud Platform (GCP) has a variety of tools to help you. Sara Robinson starts with the basics: how to use a pretrained ML model with a single API call. She then demonstrates how to customize a pretrained model with AutoML. Sara concludes by explaining how to train and serve a custom TensorFlow model on GCP. Read more.