Sponsored sessions

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.

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.

Do you have constantly changing business environments across many business units and processes with multiple job schedulers and infrastructure platforms? Do you struggle with end-to-end visibility and a lot of alerts? Award-winning ignio can help. Jayanti Murty explains how and shares real-world examples of companies that have reduced operational risks and outages and technology and labor costs. Read more.

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.

Artificial intelligence requires a low-latency, high-throughput storage system to keep the compute layer fully saturated with data. Liran Zvibel demonstrates why NVMe-optimized, distributed filesystems are ideal storage solutions to support AI applications and introduces a next-gen massively parallel shared filesystem that's NAND flash and NVMe optimized, built to solve the I/O starvation problem. Read more.

As AI grows its reach throughout society and makes decisions that affect people, any business looking to capitalize on AI’s potential must also acknowledge its impact. Kishore Durg and Teresa Escrig explain why businesses must teach and test their AI systems to act as responsible representatives so that they reflect business and societal norms of responsibility, fairness, and transparency. Read more.

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.

What if you could QA everything and make your best employees 10–100x more efficient? Ben Taylor shares real use cases of business transformation and realized value in production using deep learning and discusses some of the executive conversations and behaviors Dell EMC is seeing in the market. Read more.

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.

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.

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.

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.

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.

Google extends the "AI-first" DNA to all enterprises via the cloud. Anand Iyer walks you through the Google Cloud AI platform, from managed services to specialized ML accelerators, and shares examples of how businesses have leveraged the platform to overcome challenges in delivering customer value. Read more.

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.

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.

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.

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.

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.