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.
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.
Due to recent cyberattacks, security operations centers (SOCs) have had to focus on a holistic and cohesive security strategy by consolidating the right people, processes and technology to mitigate and remediate attacks.
This white paper, “The Five Essential Capabilities of an Analytics-Driven SOC”, dives into the necessity of SOCs to be analytics driven and how it helps IT and business leaders assess their own risk levels.
Download this white paper to to learn about:
*How advanced analytics and machine learning are now critical hallmarks of the modern security platform
*How proactively hunting and investigating threats can shore up defenses
*Why adaptive security architectures, like Splunk’s, are needed to prevent, detect and respond to attacks in today’s security landscape
In our 29-criteria evaluation of machine learning
data catalogs (MLDCs) providers, we identified
the 12 most significant ones — Alation,
Cambridge Semantics, Cloudera, Collibra,
Hortonworks, IBM, Infogix, Informatica, Oracle,
Reltio, Unifi Software, and Waterline Data —
and researched, analyzed, and scored them.
This report shows how each provider measures
up and helps enterprise architecture (EA)
professionals make the right choice.
"Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
Dive into machine learning concepts in general, as well as deep learning in particular
Understand how deep networks evolved from neural network fundamentals
Explore the major deep network architectures, including Convolutional and Recurrent
Learn how to map specific deep networks to the right problem
Walk through the fundamentals of tuning general neural networks and specific deep network architectures"
The Path to Predictive Analytics and Machine Learning This Ebook will be your guide to building and deploying scalable, production-ready machine-learning applications. Inside, you will find several machine learning use cases, code samples to help you get started, and recommended data processing architectures.
Published By: Skillsoft
Published Date: Nov 22, 2013
Cloud solutions are the “new normal” as organizations opt to deploy more business systems “in the cloud.” For learning professionals, cloud-based solutions provide a wide range of benefits from cost savings to decreased delivery time, to global reach. Before selecting a cloud-based learning provider, you should understand all the benefits and be prepared to ask potential providers critical questions regarding the architecture and management of the service you are considering.
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.
Technology’s role in business and society has
shifted away from largely driving efficiencies to
innovating and creating engaging experiences
that attract and retain customers. Innovations and
business outcomes are fueled by a perfect storm
of technology trends in cloud, analytics, machine
learning, IoT and the emerging API Economy. The
convergence of these technologies has created
new opportunities for enterprises to improve
business performance by acquiring customers
faster while creating brand loyalty. The role of
technology expands the interaction with customers
beyond the core of the enterprise – away from
100% dependencies on systems of records –
and towards real-time, contextual interactions.
Businesses are a digital business or they are
evolving to become one. This requires enterprises
to re-think how they build software architectures.
Organizations in regulated industries struggle with adopting SaaS-based learning solutions because of specific documentation, record-keeping, and IT requirements that must be followed, and the need to comply with a range of Good Practice (GxP) requirements. With a cloud solution that supports GxP requirements, companies in regulated industries can address compliance challenges and deliver a validated environment with a cloud-based LMS.
Over the past few years, business leaders have been the primary drivers of technology change, including making decisions to adopt new applications in the cloud, mandate a cloud-first strategy, offer new capabilities with an API-first strategy, and provide new applications to end users on mobile first.
There are significant benefits to these cloud decisions because they decrease time to value, lower costs, and make it easier for organizations to experiment and innovate. But there are consequences as well, chiefly in the complexity of learning how to integrate applications and exchange data across a decentralized architecture that is largely driven by autonomous development decisions.
This IDC White Paper answers the following questions about the need for hybrid integration:
How are changes in business strategy and technology adoption requiring changes in how organizations approach integration?
What are the major events that trigger integration adaptation?
How are the roles involve
DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Our portfolio of live events, online and print publishing, business intelligence and professional development brands are centred on the complexities of technology convergence. Operating in 42 different countries, we have developed a unique global knowledge and networking platform, which is trusted by over 30,000 ICT, engineering and technology professionals.
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