It's never been easier to train machine learning models. With excellent open source tooling, lower compute techniques, and incredible educational material online, really anybody can start to train their own models today. Yet, Anna Roth explains, when domain experts try to transfer their expertise to an ML model, the results can be unpredictable.
Join KC Tung to discover a way to use TensorFlow to solve a natural language processing (NLP) model bias problem with data augmentation for an enterprise customer (one of the largest airlines in the world). KC leveraged hidden gems in tf.data and the new API to easily find a novel use for text generation and found it surprisingly improved his NLP model.
Twitter employs ML throughout its product to deliver value for its customers. Theodore Summe gives you a glimpse into ML at Twitter and explains how Cortex works to accelerate ML to better serve customer needs by partnering with TensorFlow.
Maggie Zhang, Nathan Luehr, Josh Romero, Pooya Davoodi, and Davide Onofrio give you a sneak peek at software components from NVIDIA’s software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment.
Hannes Hapke leads a deep dive into deploying TensorFlow models within minutes with TensorFlow Serving and optimizing your serving infrastructure for optimal throughput.
Moderated by: Philipp Drieger (Staff Machine Learning Architect), Iman Makaremi (Principal Product Manager)
The Deep Learning Toolkit for Splunk allows you to integrate advanced custom machine learning systems with the Splunk platform using TensorFlow 2.0, PyTorch and NLP libraries. Jupyter Lab Notebooks are providing data scientists and machine learning developers with an integrated experience from rapid prototyping to operationalising models in production. The app is freely available on splunkbase.
Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari examine if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable.
Reinforce Learning can be a game changer when you do not have training data, but are instead able to simulate an environment. Unfortunately, the theory of Reinforcement Learning is complex and the vast number of algorithms in that area adds to the burden for getting started. Easyagents takes some of the burden by making it a one-liner to run a Reinforcement Learning algorithm on your problem.
Many real-world machine learning applications require generative or reductive sampling of data. Laxmi Prajapat and William Fletcher demonstrate sampling techniques applied to training and testing data directly inside the input function using the tf.data API.
Practical defense systems require precise detection during malware outbreaks with only a handful of available samples. Sean Park demonstrates how to detect in-the-wild malware samples with a single training sample of a kind, with the help of TensorFlow's flexible architecture in implementing a novel variable-length generative adversarial autoencoder.
Pengfei Fan and Lingling Jin offer an overview of an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.
Criteo's real-time bidding of ad spaces requires its TensorFlow (TF) models to make online predictions in less than 5 ms. Nicolas Kowalski and Axel Antoniotti explain why Criteo moved away from high-level APIs and rewrote its models from scratch, reimplementing cross-features and hashing functions using low-level TF operations in order to factorize as much as possible all TF nodes in its model.
Hilbert is an AI framework that works with TensorFlow Extended (TFX) at Yahoo! JAPAN, which provides AutoML to create production-level deep learning models automatically. Hilbert is currently used by over 20 services of Yahoo! JAPAN. Shin-Ichiro Okamoto details how to achieve production-level AutoML and explores service use cases at Yahoo! JAPAN.
In many applications where data is generated continuously, combining machine learning with streaming data is imperative to discover useful information in real time. Yong Tang explores TensorFlow I/O, which can be used to easily build a data pipeline with TensorFlow and stream frameworks such as Apache Kafka, AWS Kinesis, or Google Cloud PubSub.
MLIR is TensorFlow's open source machine learning compiler infrastructure that addresses the complexity caused by growing software and hardware fragmentation and makes it easier to build AI applications. Chris Lattner and Tatiana Shpeisman explain how MLIR is solving this growing hardware and software divide and how it impacts you in the future.
Jack Chung, Chao Liu, and Daniel Lowell explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as accelerated linear algebra (XLA).
Transformer architectures have taken the field of natural language processing (NLP) by storm and pushed recurrent neural networks to the sidelines. Aurélien Géron examines transformers and the amazing language models based on them (e.g., BERT and GPT 2) and shows how you can use them in your projects.
Va Barbosa and Paul Van Ec highlight the benefits of using TensorFlow.js and Node-RED together as an educational tool to engage developers and provide you with a powerful, creativity-inspiring platform for interacting and developing with machine learning models.
Jeff Dean explains why Google originally open-sourced TensorFlow almost four years ago. Join in to learn about TensorFlow's progress and how it can solve the problems you care about.
Aurélien Géron dives into creating production ML pipelines with TensorFlow Extended (TFX) and using TFX to move from ML coding to ML engineering. You'll walk through the basics and put your first pipeline together, then learn how to customize TFX components and perform deep analysis of model performance.
Kemal El Moujahid divulges exciting developments for the TensorFlow community. Join in to learn how the TensorFlow team provides new and improved resources for developers and enterprises to succeed.
Machine learning is a difficult skill to master for the many developers who are starting to use TensorFlow. Many developers use TensorFlow today, yet the majority of software developers out there have yet to learn machine learning. Mike Liang takes you through TensorFlow Hub, designed to help developers make better and faster user of machine learning in their products.
IBM has a long history of contributing to the open source projects that make the most difference to its clients, and the company has been working to build responsible solutions to enterprise data science problems for many years. Join Frederick Reiss to hear about IBM's role in open source software, TensorFlow, building AI solutions, and what IBM is excited about with this latest (2.0) release.
Get a programmer's perspective on machine learning with Laurence Moroney, from the basics all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.