Accelerating training, inference, and ML applications on NVIDIA GPUs
February 24, 2020
Maggie Zhang, Nathan Luehr, Josh Romero, Pooya Davoodi, and Davide Onofrio give you a sneak peek at software components from NVIDIAs 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.
Introduction to Hilbert AutoML with TensorFlow Extended (TFX) at Yahoo! JAPAN
February 23, 2020
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.
AI for financial time series forecasting and dynamic assets portfolio optimization
February 21, 2020
Real business usage of most advanced methods for financial time series forecasting (based on winning methods from M4 competition) and assets portfolio optimization (based on Monte Carlo Tree Search with neural networks - Alpha Zero approach). Complete investments platform with the AI workflow and real time integration with the brokers. Real usage demo.
Creating smaller, faster, production-worthy mobile machine learning models
February 20, 2020
Getting machine learning models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce arent small enough or fast enough to ship. Jameson Toole walks you through optimization, pruning, and compression techniques to keep app sizes small and inference speeds high.
Executive Briefing: Fusing data and design
February 19, 2020
Data scientists feel naturally comfortable with the language of mathematics, while designers think in the language of human empathy. Creating a bridge between the two was essential to the success of a recent project at an energy company. Tim Daines and Philip Pilgerstorfer detail what they learned while creating these bridges, showcasing techniques through a series of aha moments.
Data science and the business of Major League Baseball
February 16, 2020
Using SAS, Python, and AWS SageMaker, Major League Baseball's (MLB's) data science team outlines how it predicts ticket purchasers likelihood to purchase again, evaluates prospective season schedules, estimates customer lifetime value, optimizes promotion schedules, quantifies the strength of fan avidity, and monitors the health of monthly subscriptions to its game-streaming service.
Improving Embedded Systems Boot Time by Hibernation: An Overview on the State of the Art and a Case of Study on i.MX family of Processors
February 16, 2020
Improving boot time is always a delicate matter and literature is very rich. Linux based OSes benefit from standard optimization approaches however, Android is still far away from having exciting resu …
From whiteboard to production: A demand forecasting system for an online grocery shop
February 13, 2020
Data-driven software is revolutionizing the world and enable intelligent services we interact with daily. Robert Pesch and Robin Senge outline the development process, statistical modeling, data-driven decision making, and components needed for productionizing a fully automated and highly scalable demand forecasting system for an online grocery shop for a billion-dollar retail group in Europe.
Hands-on machine learning with Kafka-based streaming pipelines
February 13, 2020
Boris Lublinsky and Dean Wampler examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more.
How machine learning meets optimization
February 12, 2020
Machine learning and constraint-based optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. But Jari Koister examines how they're similar, how they're different, and how they can be used to solve complex problems with amazing results.
Your easy move to serverless computing and radically simplified data processing
February 7, 2020
Most analytic flows can benefit from serverless, starting with simple cases to and moving to complex data preparations for AI frameworks like TensorFlow. To address the challenge of how to easily integrate serverless without major disruptions to your system, Gil Vernik explores the push to the cloud experience, which dramatically simplifies serverless for big data processing frameworks.
Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs
February 6, 2020
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.
Getting the AI you want on the infrastructure you know: 2 deep-dive case studies of AI on CPU
February 5, 2020
Kushal Datta specializes in optimizing AI applications on CPUs; hear two of his latest customer success stories and get the details behind the technical collaboration that led to incredible performance for AI on CPU.
On gradient-based methods for finding game-theoretic equilibria
February 4, 2020
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Michael Jordan details the aim to blend gradient-based methodology with game-theoretic goals as part of a large "microeconomics meets machine learning" program.
5G RAN Optimization Using the O-RAN Software Communitys RIC (RAN Intelligent Controller)
January 8, 2020
The O-RAN SCs RIC (RAN Intelligent Controller) is an open-source container-based implementation of the RIC concept as standardized by O-RAN. The presentation will give an overview of its architecture …
Processing 10M samples a second to drive smart maintenance in complex IIoT systems
January 7, 2020
Geir Engdahl and Daniel Bergqvist explain how Cognite is developing IIoT smart maintenance systems that can process 10M samples a second from thousands of sensors. You'll explore an architecture designed for high performance, robust streaming sensor data ingest, and cost-effective storage of large volumes of time series data as well as best practices learned along the way.
The digital truth and the physical twin
January 5, 2020
The truth is no longer what you see with your eyes; the truth is in the digital sphere, where it only sometimes needs a physical twin. After all, what's the need for a road sign along the street if the information is already in the car? Simon Moritz details how the Fourth Industrial Revolution is transforming companies and business models as we know it.