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Building Fast Data Compression Code for Cloud and Edge Applications

Finding efficient ways to compress and decompress data is more important than ever. Compressed data takes up less space and requires less time and network bandwidth to transfer. In this article, we’ll discuss the data compression functions and the latest improvements in the Intel® Integrated Performance Primitives (Intel® IPP) library.

Book Review: Python Data Science Handbook

I recently had a need for a Python language resource to supplement a series of courses on Deep Learning I was evaluating that depended on this widely used language. As a long-time data science practitioner, my language of choice has been R, so I relished the opportunity to dig into Python to see first hand how the other side of the data science world did machine learning. The book I settled on was “Python Data Science Handbook: Essential Tools for Working with Data” by Jake VanderPlas.

How Can We Trust Machine Learning?

How Can We Trust Machine Learning? In this talk, Carlos Guestrin, CEO of Dato, Inc. and Amazon Professor of Machine Learning at the University of Washington, describes recent research and new tools with which companies can start to have the means to gain trust and confidence in the models and predictions behind their core business applications.

Zaloni Introduces Data Lake with New Machine Learning Data Matching Technology

As an extension to its Data Lake Management Platform, Zaloni has introduced a machine-learning data matching engine, which leverages the data lake to create “golden” records and enable enriched data views for multiple use cases across business sectors.

Solutions for Autonomous Driving – From Car to Cloud

From car to cloud―and the connectivity in between―there is a need for automated driving solutions that include high-performance platforms, software development tools, and robust technologies for the data center. With Intel GO automotive driving solutions, Intel brings its deep expertise in computing, connectivity, and the cloud to the automotive industry.

Oxford Research Validates EarlySign’s AI Platform for Identifying Risk of Colorectal Cancer

Medial EarlySign, a developer of machine learning tools for data-driven medicine, announced the results of new research with Oxford University. The study provides further validation for Medial EarlySign’s ColonFlagTM algorithm platform to identify individuals at risk of having colorectal cancer and support other approaches to early detection, including screening and active case finding.

Sibyl Launches To Increase Utilization And Cut Impact of Costly Patient No-Shows

The founders of macro-eyes, a machine learning company that simplifies personalized patient care, announced the introduction of Sibyl, a predictive scheduling solution that cuts the financial and operational damage from patient No-Shows without relying on patient behavior change.

Interview: David Steinmetz, Machine Learning Engineer at Capital One

I recently caught up with Daniel Steinmetz, who is a Machine Learning Engineer with Capital One Bank to discuss how to get a job at Capital One, the types skills they are looking for, and what his typical day looks like.

The Importance of Vectorization Resurfaces

Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.

AI is on Its Way to the Enterprise, Bringing Easy Analytics with It

In this contributed article, Doug Bordonaro, Chief Data Evangelist at ThoughtSpot takes a personal ride through all the ways that artificial intelligence (AI) is making strong inroads into the enterprise, specifically how the promise of AI in the enterprise is finally being realized.