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More to Machine Learning than Meets the Eye

In this special guest feature, Kevin Gidney, Co-Founder and CTO at Seal Software​, explores four main factors that go into creating advanced machine learning technology. There is a lot of required training and work that goes into developing successful machine learning solutions and not all ML is created equal.

Enterprise Applications of the R Language (EARL) Conference – San Francisco

It’s the home of big name companies like Google, Salesforce, Twitter, Uber, Airbnb and Yelp. San Francisco is the perfect place to celebrate and share the power of R. The Enterprise Applications of the R Language (EARL) Conference is happening in San Francisco on June 5-7, 2017.

Interview: Matt Winkler, Group Program Manager for Machine Learning at Microsoft

In this podcast interview, we caught up with Matt Winkler, Group Program Manager for Machine Learning at Microsoft, to get his take on the upward trajectory of data science, machine learning and the cloud – specifically Azure. Matt leads a team crafting tools and services to enable data scientists and developers to do more with their data. Originally from St. Louis, Matt has been at Microsoft for 11 years working on developer tools and cloud services such as the .NET Framework, Visual Studio, Azure Websites, Data Lake and HDInsight.

3 Major Impacts of Machine Learning on Manufacturing Today

In this special guest feature, Gary Brooks, CMO at Syncron, outlines three areas where manufacturers can incorporate machine learning today, and start seeing immediate results.

7 Steps From Raw Data to Insight

Data scientists generally ascribe to the “machine learning process” which is seen as a roadmap to follow when working on a data science project. The infographic at the end of this article provides a detailed work flow that it is general enough to encompass pretty much any data science project.

Enterprise Software Tools for AI

This post delves into a variety of enterprise software options for AI. This article is part of a special insideHPC report that explores trends in machine learning and deep learning. Find out how businesses are using machine learning and deep learning, differentiating between AI, machine learning and deep learning, what it takes to get started, software tools for AI and more.

Gain a Competitive Advantage With Machine Learning

In this contributed article, tech writer Linda Gimmeson outlines a few ways any company can be more competitive with the help of machine learning.

Configuration for Big-Data-as-a-Service

This white paper describes a new solution for Big-Data-as-a-Service combining the BlueData EPIC (Elastic Private Instant Clusters) software platform with the HPE Elastic Platform for Big Data Analytics (EPA). BlueData is transforming how enterprises deploy their Big Data applications and infrastructure. To learn more about Big-Data-as-a-Service download this white paper.

Differentiating between AI, Machine Learning and Deep Learning

With all the quickly evolving jargon in the industry today, it’s important to be able to differentiate between AI, machine learning and deep learning. This article is part of a special insideHPC report that explores trends in machine learning and deep learning.

Machine Learning Is Over-hyped and It Is Vital We Start To Cut Through The Noise

Businesses are ultimately still at an early stage with their machine learning adoption and understanding its capabilities. More than 20 experts from world-leading organizations will speak at the upcoming Machine Learning Innovation Summit, which will take place this June 5-6 at the Marriott Union Square in San Francisco. Speakers from a diverse range of industries will take the stage.