IDG Contributor Network: With deep learning, the data-rich get richer

In a previous post I discussed the promising applications for deep learning in the enterprise. The greatest potential for deep learning is in adding business-relevant structure to less-structured, sense-like data — such as images, audio and other sensor data.

How quickly does the tone and affect of a support call from a frustrated customer change, broken down by support rep? It’s that time-to-mollification that matters to your business, not the raw sound data.

Generally when training machine learning algorithms (and deep nets are an extreme example of this), the more data the better. There’s a persistent danger of “overfitting” your data — performing very well on the training set, but poorly on new data. If the algorithm has overfit, it has failed to generalize and is thus not that useful.

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Computerworld Cloud Computing

Google machine learning gains Kaggle and more

Google has already carved out a niche for itself in machine learning with projects like TensorFlow and Google Brain. Now, it’s adding data science provider Kaggle, which runs contests related to machine learning and provides services for data discovery and analysis, to the fold. The company also is moving ahead with other machine learning projects, including an API providing intelligence for video.

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InfoWorld Cloud Computing

Cloud and data center trends roundup 2016: Machine learning, hybrid cloud and Google’s enterprise ambitions

A decade on from the launch of Amazon Web Services (AWS), the cloud market is continuing to evolve quickly. What was once seen as a toy for test and development purposes now hosts mission-critical workloads for some of the largest companies in the world, while vendors work on the next generation of cloud services, such as those around machine learning.

Business demand clearly shows no sign of abating. Gartner claimed the overall cloud market was valued at $ 208.6 billion in 2016, amounting to a 17.2 percent increase from $ 178 billion the year before.

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CIO Cloud Computing

Google goes all in on cloud machine learning with new services

There’s an arms race among public cloud providers to provide businesses with the best machine learning capabilities. Enterprises are increasingly interested in creating intelligent applications, and companies like Amazon, Microsoft, and Google are rushing to help meet their needs.

Google fired its latest salvo on Tuesday, announcing a set of enhancements to its existing suite of cloud machine learning capabilities. The first was a new Jobs API aimed at helping match job applicants with the right openings. In addition, the company is slashing the prices on its Cloud Vision API and launching an enhanced version of its translation API.

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InfoWorld Cloud Computing

How to approach machine learning in the cloud

Artificial intelligence and its machine learning subset are all the rage these days. That was evident when I spoke this week at the AI World event, which was packed with vendors and users seeking to understand what the hell AI and machine learning are—and wanting to know how they could use this old but revitalized technology effectively.

Amazon Web Services, Google, IBM, Microsoft, and the other major cloud providers all have machine learning services in their clouds now. But most enterprises have no clue on what the heck to do with machine learning systems, whether cloud or on-premises. Here’s some quick guidance.

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InfoWorld Cloud Computing

Could machine learning help Google’s cloud catch up to AWS and Azure?

Google has been offering public cloud services for several years now, but the company has continued to lag behind Amazon and Microsoft in customer growth. 

Under the leadership of VMware co-founder Diane Greene, who serves as the executive vice president of Google Cloud Enterprise, the tech titan has focused harder on forging partnerships and developing products to appeal to large customers. It has added a number of key customers under Greene’s tenure, including Spotify.  

One such win is Evernote, which announced Tuesday it would be migrating its service away from its private data centers and to Google’s public cloud. When Evernote was looking for a public cloud provider, the company was interested in not only the base level infrastructure available, but also high-level machine learning services and services for building machine learning-driven systems, said Anirban Kundu, Evernote’s CTO.

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CIO Cloud Computing

Review: Amazon puts machine learning in reach

As a physicist, I was originally trained to describe the world in terms of exact equations. Later, as an experimental high-energy particle physicist, I learned to deal with vast amounts of data with errors and with evaluating competing models to describe the data. Business data, taken in bulk, is often messier and harder to model than the physics data on which I cut my teeth. Simply put, human behavior is complicated, inconsistent, and not well understood, and it’s affected by many variables.

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