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Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

HPE Accelerates Artificial Intelligence Innovation with Enterprise-grade Solution for Managing Entire Machine Learning Lifecycle

Hewlett Packard Enterprise (HPE) announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days.

Interview: Terry Deem and David Liu at Intel

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel® Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel® Distribution for Python*, both from Intel, to discuss the Intel® Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution’s use in scientific computing, and a look to the future with respect to IPD.

Using Artificial Intelligence to Track Birds’ Dark-of-Night Migrations

On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services’ network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers.

Composable Multi-Threaded Parallelism in Julia

JuliaCon 2019, held July 22-26, 2019 at the University of Maryland in Baltimore, was the biggest and best JuliaCon to date. The JuliaCon session below, “Composable Multi-Threaded Parallelism in Julia,” was presented by Jeff Bezanson and Jameson Nash (Julia Computing). The talk discusses the release of a preview of an entirely new threading interface for Julia programs: general task parallelism.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

XAIN Puts AI Privacy First, at No Cost to Efficiency, with its Distributed AI Solution

XAIN, the AI startup that specializes in privacy-oriented Federated Machine Learning (FedML), is developing an infrastructure to train artificial intelligence applications through FedML technology, a mechanism that emphasizes data privacy. XAIN’s distributed approach to machine learning, which intends to comply with the European Commission’s General Data Protection Regulations (GDPR), also provides greater efficiency in the way data is trained, marking a major breakthrough in a field otherwise burdened by costly and onerous processes.

Field Report: KDD 2019

As a very long time member of the ACM and their SIGKDD group, I’d always wanted to attend a KDD conference (first one occurred in 1995). This year I received a gracious invitation to attend KDD2019 in Anchorage, Alaska, August 4-8. It satisfied two of my bucket list items: witnessing a KDD first-hand and also […]

Help! My Data Scientists Can’t Write (Production) Code!

In this contributed article, Nisha Talagala, Co-founder and CTO/VP of Engineering at ParallelM, takes a hard look at productionizing machine learning code and how integrating SDLC practices with MLOps (production ML) practices certifies that all code, ML or not, is managed, tracked and executed safely.

Why You Need a Modern Infrastructure to Accelerate AI and ML Workloads

Recent years have seen a boom in the generation of data from a variety of sources: connected devices, IoT, analytics, healthcare, smartphones, and much more. This data management problem is particularly acute in the areas of Artificial Intelligence (AI) and Machine Learning (ML) workloads. This guest article from WekaIO highlights why focusing on optimizing infrastructure can spur machine learning workloads and AI success.