Machine Learning

Hybrid and Dedicated Clouds to Help Drive Massive Growth for Machine Learning Projects Over Next Two Years

Grazed from Univa

Machine learning (ML) is poised for explosive growth over the next two years with an increasing number of projects moving into production by 2020, based on a recent survey of more than 344 technology and IT professionals titled "The Future of Machine Learning," conducted by technology marketing organization, Dimensional Research. Though a diverse set of ML projects are currently initiated by 93% of the respondents, only 22% of these projects have actually moved into production, citing migration as the top technical challenge.

Univa, the leading innovator in on-premise and hybrid cloud workload management solutions for enterprise HPC customers, sponsored the survey that polled 344 technology and IT professionals across the globe and across 17 industries, with technology, financial services and healthcare leading the charge in ML adoption. "Our customers are already asking for guidance with migrating their HPC and machine learning workloads to the cloud or hybrid environment," said Rob Lalonde, vice president and general manager of Navops at Univa. "As a result, we decided to conduct this survey to better understand the type of projects driving value in machine learning, as well as better assess what key challenges users are currently facing that are preventing them from moving their projects into production. We look forward to utilizing this data to help guide our customers and recommend the right set of tools and migration options needed to accelerate ML value."

SIOS Technology Showcases Early Adaptation of Machine Learning for High Availability Challenges in the Cloud at AWS Summit

Grazed from SIOS

SIOS Technology Corp., the industry pioneer in providing intelligent application availability for critical workloads, today announced that it will showcase advanced research in the application of Machine Learning (ML) aimed at lowering cloud costs while optimizing High Availability (HA) for critical applications operating in the cloud at AWS Summit in Atlanta, GA on September 13 in Booth #612. SIOS is an exhibiting Silver Sponsor.

"The move from datacenter to the cloud is well underway for many applications, but the migration of critical applications based on SAP, SQL Server and Oracle has just begun," said Jerry Melnick, president and CEO, SIOS Technology. "At SIOS, we have a long history delivering HA software to ensure availability and we've developed leading-edge machine learning solutions to help IT understand and optimize computing infrastructures. At the show, we'll give attendees a peek into the future of how these technologies used together can deliver the confidence and cost savings that will be essential for operating in the cloud."

Webinar: 451 Research - Key Trends in Machine Learning, AI and Cloud

 

 

Machine learning (ML) and artificial intelligence (AI) aren't new - both are ideas that emerged in the 1950's. But after incubating for 50+ years, it's only in the past few that they have made unprecedented advances in capability, scalability and accessibility.  

Make sure to register and join Zenoss and Nancy Gohring, senior analyst at 451 Research, for a live 45-minute webinar Wednesday, Sept. 19, 11 a.m. CDT, where they will discuss key trends in machine learning and artificial intelligence, the impact of cloud computing on ML and AI and what it all means for IT Ops and DevOps teams.

They will dive into:

  • Digital transformation, "Fast IT," and the rampant risks
  • Trends for ML and AI and the impact they have on IT
  • Cloud computing and its implications for observability
  • Contextualizing all data for faster, safer IT

Register today to learn more about leveraging modern technologies to reduce the risk of digital transformation.

MapR Academy Delivers New Machine Learning Course, Free and On-Demand

Grazed from MapR

MapR Technologies, Inc., provider of the industry's leading data platform for AI and Analytics, today announced a new, free introductory course from MapR Academy on Artificial Intelligence (AI) and Machine Learning (ML).  This on-demand course provides insights into how businesses can leverage AI and ML to improve operations through real-world use cases.  This course is ideal for developers just starting out in ML, as well as higher-level business decision makers.

"Machine learning, a trending topic in big data, is not fully understood.  This course provides a foundation for anyone curious about ML/AI and how it works in practical applications," said Suzanne Ferry, vice president, global training and enablement, MapR.  "Decision-makers, even those who already use big data, will benefit from learning more about common ML/AI business use cases."

 

Major League Baseball Selects AWS as its Official Provider for Machine Learning, Artificial Intelligence, and Deep Learning

Grazed from Amazon

Amazon Web Services, Inc. (AWS), an Amazon.com company, announced that Major League Baseball (MLB) has chosen AWS as its official provider for machine learning, artificial intelligence, and deep learning workloads. In extending its long-standing relationship, MLB will use AWS machine learning services to continue development of Statcast-the tracking technology that runs on AWS to analyze player performance for every game-and develop new technologies to support MLB Clubs in driving innovative fan experiences and engagement across all 30 Major League ballparks. In addition, MLB will work with the Amazon ML Solutions Lab to amplify game statistical data integrations within broadcasts, including MLB Network, and live digital distribution, such as MLB.com and the MLB At Bat app, using machine learning, creating more personalized viewer experiences tailored for each market and geographic region.

Formula 1 Selects AWS as its Official Cloud and Machine Learning Provider

Grazed from Amazon Web Services

Amazon Web Services, Inc. (AWS), an Amazon.com company, announced that Formula One Group (Formula 1) is moving the vast majority of its infrastructure from on-premises data centers to AWS, and standardizing on AWS's machine learning and data analytics services to accelerate its cloud transformation. Formula 1 will work with AWS to enhance its race strategies, data tracking systems, and digital broadcasts through a wide variety of AWS services-including Amazon SageMaker, a fully managed machine learning service that enables everyday developers and scientists to easily build and deploy machine learning models, AWS Lambda, AWS's pioneering event-driven serverless computing service, and AWS analytics services-to uncover never-before-seen metrics that will change the way fans and teams enjoy, experience, and participate in racing. Formula 1 has also selected AWS Elemental Media Services to power its video asset workflows, enhancing the viewing experience for its 500 million plus fans worldwide.

BlueData Introduces New Innovations for AI and Machine Learning in Hybrid or Multi-Cloud Deployments

Grazed from BlueData

BlueData, provider of the leading Big-Data-as-a-Service (BDaaS) software platform, today announced the new summer release for BlueData EPIC. This release builds upon BlueData's innovations in running large-scale distributed analytics and machine learning (ML) workloads on Docker containers, with new functionality to deliver even greater agility and cost savings for enterprise Big Data and AI initiatives. 

Last spring, BlueData introduced support for hybrid cloud environments - leveraging the inherent infrastructure portability and flexibility of Docker containers. This past fall, BlueData delivered a major new release that added deep learning (DL), GPU acceleration, and multi-cloud support to the container-based BlueData EPIC platform. And last month, BlueData announced a new turnkey solution to accelerate AI and ML / DL deployments in the enterprise.

 

Protecting Your Organization's Cloud

As organizations move to the cloud, the next evolution of Network Access Control takes shape to meet business demands.
 

What are the chances that your organization has moved some of its data, systems, programs and applications to the cloud? Quite high. According to a Synergy Research survey of IT professionals, spending on private cloud and cloud-enabled solutions grew by 16% between the second quarters of 2015 to 2017. During that same period, traditional, non-cloud data center hardware and software dropped 18%. It is easy to understand why these days, cyber security threats are coming in through many new channels and vulnerabilities that are not part of the traditional IT infrastructure such as, mobile devices and cloud-based applications. The following prevalent trends in the enterprise highlight why it is essential to monitor your network with a Cloud Network Access Control (Cloud-Based NAC). 

Company Networks Go Global

Organizations are becoming perimeter-less. Traditional IT perimeters have been torn down by the adoption of BYOD, IoT devices, telecommuting and cloud computing. Therefore, one can no longer look at the network as a defined infrastructure within a physical firewall. The network is essentially global without boundaries. According to a Gallop News Service poll from 2015, 37% of U.S. workers had telecommuted for work, and this was up by 30% from the last decade but four times greater than the 9% found in 1995. Perimeter-less companies require continuous risk-monitoring and risk-assessment that can match the ever increasing mobile and cloud-based reality and this can be performed best via the cloud. 

Tens of Thousands of Customers Flocking to AWS for Machine Learning Services

Grazed from Amazon AWS

Today, Amazon Web Services, Inc. (AWS) shared that tens of thousands of customers are using AWS machine learning services, with active users increasing more than 250 percent in the last year, spurred by the broad adoption of Amazon SageMaker since AWS re: Invent 2017. Amazon SageMaker is a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the machine learning process, empowering everyday developers and scientists to use machine learning much more expansively and successfully. AWS has meaningfully more reference customers for machine learning than any other provider, and much of it has to do with AWS's unmatched array of services that enable a full stack machine learning experience. With AWS machine learning services, customers are building a wide variety of intelligent applications and solutions with the help of AWS's P2 and P3 graphical processing unit (GPU) instances, deep learning Amazon Machine Images (AMIs) that embed all the major frameworks, Amazon SageMaker, AWS DeepLens-a device that has helped thousands of customers gain hands on experience with machine learning, and services at the top layer of the stack such as Amazon Rekognition, Amazon Polly, Amazon Lex, and Amazon Comprehend.

Today, AWS also announced the general availability of two new machine learning services, which are part of AWS's machine learning portfolio, Amazon Transcribe and Amazon Translate. Amazon Transcribe provides grammatically correct transcriptions of audio files to allow audio data to be analyzed, indexed, and searched. Amazon Translate is a deep learning powered machine translation service that provides natural sounding language translation in both real-time and batch scenarios. These services further extend the language capabilities already provided on AWS with Amazon Lex for conversational interfaces, Amazon Polly for Text-to-Speech, and Amazon Comprehend for processing natural language to discover insights and contextual relationships in text.

OpenStack 'Queens' Release Expands Support for GPUs and Containers to Meet Edge, NFV and Machine Learning Workload Demands

Grazed from OpenStack

The OpenStack community today released Queens, the 17th version of the most widely deployed open source cloud infrastructure software. A packed release resulting from a six-month development cycle, Queens offers advancements benefiting not only enterprises with mission-critical workloads but also organizations investing in emerging use cases like containers, NFV, edge computing and machine learning. The software now powers 60 public cloud data centers and thousands of private clouds at a scale of more than six million physical cores.  

Enterprise adoption of cloud continues to expand, and by next year, 60 percent of enterprise workloads will run in the cloud, according to 451 Research's Voice of the Enterprise: Cloud Transformation, Workloads and Key Projects survey. 451 Research also reports that enterprise adoption of OpenStack is expanding in parallel, with enterprises in nearly all verticals and regions now running mission-critical workloads on OpenStack software. To support these workloads, the Queens release includes robust, enterprise-oriented features, most notably the multi-attach feature in Cinder.