machine learning

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Published By: DATAVERSITY     Published Date: Jul 24, 2014
Will the “programmable era” of computers be replaced by Cognitive Computing systems which can learn from interactions and reason through dynamic experience just like humans? With rapidly increasing volumes of Big Data, there is a compelling need for smarter machines to organize data faster, make better sense of it, discover insights, then learn, adapt, and improve over time without direct programming. This paper is sponsored by: Cognitive Scale.
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data, data management, cognitive computing, machine learning, artificial intelligence, research paper
    
DATAVERSITY
Published By: Wave Computing     Published Date: Jul 06, 2018
This paper argues a case for the use of coarse grained reconfigurable array (CGRA) architectures for the efficient acceleration of the data flow computations used in deep neural network training and inferencing. The paper discusses the problems with other parallel acceleration systems such as massively parallel processor arrays (MPPAs) and heterogeneous systems based on CUDA and OpenCL, and proposes that CGRAs with autonomous computing features deliver improved performance and computational efficiency. The machine learning compute appliance that Wave Computing is developing executes data flow graphs using multiple clock-less, CGRA-based System on Chips (SoCs) each containing 16,000 processing elements (PEs). This paper describes the tools needed for efficient compilation of data flow graphs to the CGRA architecture, and outlines Wave Computing’s WaveFlow software (SW) framework for the online mapping of models from popular workflows like Tensorflow, MXNet and Caffe.
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Wave Computing
Published By: MapR Technologies     Published Date: Aug 01, 2018
How do you get a machine learning system to deliver value from big data? Turns out that 90% of the effort required for success in machine learning is not the algorithm or the model or the learning - it's the logistics. Ted Dunning and Ellen Friedman identify what matters in machine learning logistics, what challenges arise, especially in a production setting, and they introduce an innovative solution: the rendezvous architecture. This new design for model management is based on a streaming approach in a microservices style. Rendezvous addresses the need to preserve and share raw data, to do effective model-to-model comparisons and to have new models on standby, ready for a hot hand-off when a production model needs to be replaced.
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MapR Technologies
Published By: Skytree     Published Date: Nov 23, 2014
Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. In this brief, discover how you can use machine learning to analyze both unstructured text data and semi- structured log data, providing you with the insights needed to achieve your business goals.
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log data, machine learning, natural language, nlp, natural language processing, skytree, unstructured data, semi-structured data, data analysis
    
Skytree
Published By: Reltio     Published Date: May 22, 2018
"Forrester's research uncovered a market in which Reltio [and other companies] lead the pack,” the Forrester Wave Master Data Management states. "Leaders demonstrated extensive and MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.” Reltio executes the vision for next-generation MDM by converging trusted data management with business insight solutions at scale and in the cloud. Machine learning and graph technology capabilities enable a contextual data model while also maintaining temporal and lineage changes of the master data.
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Reltio
Published By: Reltio     Published Date: Nov 16, 2018
Big data is growing faster than the capabilities available to manage and analyze it. Get this vendor comparison to learn how a modern master data management platform will help you to achieve better outcomes.
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Reltio
Published By: Syncsort     Published Date: Jul 17, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: Syncsort     Published Date: Oct 25, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: Attivio     Published Date: Mar 14, 2018
Did you ever consider all of the examples of machine learning in your personal life? Google’s page ranking system, photo tagging on Facebook, and customized product recommendations from Amazon are all driven by machine learning under the hood. How do these same techniques improve productivity for your business? Search is the new data and content curation. Improved relevance translates to faster search results and better business outcomes across the line. Download the Five-Minute Guide to Machine Learning to find out how self-learning technologies drive increasingly relevant answers and better context for cognitive search.
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Attivio
Published By: Converseon     Published Date: Apr 02, 2018
Separating signals from noisy social listening data has long been a problem for data scientists. Poor precision due to slag, sarcasm and implicit meaning has often made it too challenging to effectively model. Today, however, new approaches that leverage active machine learning are rapidly over taking aging rules-based techniques and opening up use of this data in new and important ways. This paper provides some detail on the evolution of text analysis including current best practices and how AI can be used by data scientists to use this data for meaningful analysis.
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Converseon
Published By: Semantic Web Company     Published Date: Jun 27, 2018
Get a comprehensive introduction to AI technologies and learn why semantics should be a fundamental element of any AI strategy. Semantic enhanced artificial intelligence (Semantic AI) is based on the fusion of semantic technologies and machine learning. In this white paper, you will understand how to align the work of data scientists and subject matter experts to increase the business value of your data lake.
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Semantic Web Company
Published By: Workday     Published Date: Oct 11, 2018
Artificial intelligence (AI) and machine learning are redefining business analytics. But for HR, use cases can be much more complex. Learn five key steps to build a strong foundation for answering HCM questions today and position yourself to use AI in HR going forward.
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Workday
Published By: Adobe     Published Date: Oct 11, 2018
Adobe offers powerful personalization tools that help you give your customers custom experiences every time they interact with you. With Adobe, you can take control of your data, use AI to achieve scale, and see incredible results. Adobe Target helps marketers deliver relevant, personalized experiences to highly targeted audiences based on behavioral analytics and audience data. Powered by AI and machine learning, users can deliver individualized customer experiences at massive scale.
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Adobe
Published By: Reltio     Published Date: Nov 20, 2018
Big data is growing faster than the capabilities available to manage and analyze it. Get this vendor comparison to learn how a modern master data management platform will help you to achieve better outcomes.
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Reltio
Published By: SAS     Published Date: Aug 28, 2018
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever. Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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SAS
Published By: SAS     Published Date: Oct 03, 2018
Unlike rules-based systems, which are fairly easy for fraudsters to test and circumvent, machine learning adapts to changing behaviors in a population through automated model building. With every iteration, the algorithms get smarter and more accurately find activities that represent risk to the firm.
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SAS
Published By: SAS     Published Date: Oct 03, 2018
Fraudsters are only becoming smarter. How is your organization keeping pace and staying ahead of fraud schemes and regulatory mandates to monitor for them? Technology is redefining what’s possible in fighting fraud and financial crimes, and SAS is at the forefront, offering solutions to: • Protect from reputational, regulatory and financial risks. • Reduce the cost of fraud and financial crimes prevention. • Gain a holistic view of risk across functions. • Include cyber events in regulatory report filings. In this e-book, learn the basics in how to prevent fraud, achieve compliance and preserve security. SAS fraud solutions use advanced analytics and artificial intelligence to help your organization better detect and prevent fraud. By applying analytics and powerful machine learning on a unifying platform, SAS helps organizations around the globe detect more financial offenses, reduce false positives and run more efficient investigations.
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SAS
Published By: Entrust Datacard     Published Date: Mar 20, 2017
As digital business evolves, however, we’re finding that the best form of security and enablement will likely remove any real responsibility from users. They will not be required to carry tokens, recall passwords or execute on any security routines. Leveraging machine learning, artificial intelligence, device identity and other technologies will make security stronger, yet far more transparent. From a security standpoint, this will lead to better outcomes for enterprises in terms of breach prevention and data protection. Just as important, however, it will enable authorized users in new ways. They will be able to access the networks, data and collaboration tools they need without friction, saving time and frustration. More time drives increased employee productivity and frictionless access to critical data leads to business agility. Leveraging cloud, mobile and Internet of Things (IoT) infrastructures, enterprises will be able to transform key metrics such as productivity, profitabilit
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Entrust Datacard
Published By: Cisco EMEA     Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
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big data, analytics, virtualization, cloudera, ibm, sas, sap, splunk
    
Cisco EMEA
Published By: Box     Published Date: Jun 22, 2018
• Facing a myriad of challenges from digital transformation, business today are making big bets on the best collaboration tools they need on hand to meet those challenges. From employee buy-in, to machine-learning capabilities, to security, it's important to select a service with the right capabilities to further your business goals. The challenge, however, is that with so many services to choose from it can be difficult to figure out which one is the right fit for your business. • This eBook, 5 Considerations in Choosing a Collaboration Platform in the Digital Age, will walk you through the ins and outs of what to keep in mind as you choose the best collaboration platform for you.
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Box
Published By: Hewlett Packard Enterprise     Published Date: Mar 26, 2018
Business users expect immediate access to data, all the time and without interruption. But reality does not always meet expectations. IT leaders must constantly perform intricate forensic work to unravel the maze of issues that impact data delivery to applications. This performance gap between the data and the application creates a bottleneck that impacts productivity and ultimately damages a business’ ability to operate effectively. We term this the “app-data gap.”
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Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: Jul 19, 2018
"This research by Nimble Storage, a Hewlett Packard Enterprise Company, outlines the top five causes of application delays. The report analyzes more than 12,000 anonymized cases of downtime and slow performance. Read this report and find out: Top 5 causes of downtime and poor performance across the infrastructure stack How machine learning and predictive analytics can prevent issues Steps you can take to boost performance and availability"
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cloud, nimble storage, infrastructure
    
Hewlett Packard Enterprise
Published By: Oracle     Published Date: Jan 16, 2018
Download this webinar to learn about Machine Learning and how it can be applied to finance, and acquire basic fundamentals about how to begin the journey.
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Oracle
Published By: Dell EMC     Published Date: Oct 13, 2016
Flexibility is important, since many future initiatives—big data, machine learning, emerging technologies, and new business directions—will be built on this cloud structure. No matter what shape your cloud infrastructure takes, Dell EMC converged and hyper-converged platforms and innovations like Dell EMC VscaleTM Architecture, powered by Intel® Xeon® processors, deliver the pathways to scale-up and scale-out, today and tomorrow.
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emc, capex, opex, roi, hyper-converged platforms
    
Dell EMC
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