nGraph: Unlocking next-generation performance with deep learning compilers
January 4, 2020
The rapid growth of deep learning in demanding large-scale real-world applications has led to a rapid increase in demand for high-performance training and inference solutions. Adam Straw, Adam Procter, and Robert Earhart offer a comprehensive overview of Intel's nGraph deep learning compiler.
Best practices for scaling modeling platforms
January 2, 2020
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry.
Deep learning for recommender systems, Or How to compare pears with apples
January 1, 2020
Recommender systems support decision making with personalized suggestions and have proven useful in ecommerce, entertainment, and social networks. Sparse data and linear models are a burden, but the application of deep learning sets new boundaries and offers remarkable results. Join Marcel Kurovski to explore a use case for vehicle recommendations at Germany's biggest online vehicle market.
ML at Twitter: A deep dive into Twitter's timeline
December 29, 2019
Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has madefrom modeling to infrastructurein order to have models that are both expressive and efficient.
Applied machine learning in finance
December 27, 2019
Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Chakri Cherukuri explains how machine learning and deep learning techniques are being used in quantitative finance and details how these models work under the hood.
Faster ML over joins of tables
December 24, 2019
Arun Kumar details recent techniques to accelerate ML over data that is the output of joins of multiple tables. Using ideas from query optimization and learning theory, Arun demonstrates how to avoid joins before ML to reduce runtimes and memory and storage footprints. Along the way, he explores open source software prototypes and sample ML code in both R and Python.
Lightning Talk: Kuber-What-Es?! Misadventures in Building UIs for K8s-Based ML Platforms
December 24, 2019
Last year, our team set out to build a machine learning platform for launching hyperparameter optimization jobs. However, after our launch, the kubernetes-based machine learning platform wasn't a hit …
A Method for the Cost Optimization of Kubernetes-based Deep Learning Training and Inference
September 26, 2019
To improve the throughput capacity of the training or inference applications without adding extra GPU cores, we share one GPU core between multiple deep learning workloads in a kubernetes cluster by c …