Detect attacks that get past perimeter defenses across the digital business. Detect malicious patterns in encrypted traffic. No decryption is needed with our Encrypted Traffic Analytics technology and multilayer machine learning. Extend your network visibility.
As the world of traditional manufacturing fuses with information technology, organizations are tapping into a level of technical orchestration never attainable before. Symphonies of systems facilitate real - time interactions of people, machines, assets, systems, and things. This is the Smart Factory; the factory ecosystem of the future. It is an application of the Industrial Internet of Things (IIoT) built with sets of hardware and software that collectively enable processes to govern themselves through machine learning and cognitive computing
Massive amounts of data are being created driven by
billions of sensors all around us such as cameras, smart
phones, cars as well as the large amounts of data across
enterprises, education systems and organizations. In
the age of big data, artificial intelligence (AI), machine
learning and deep learning deliver unprecedented
insights in the massive amounts of data.
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.
Published By: IBM APAC
Published Date: Oct 16, 2018
The latest IBM POWER9 Server is built for the most demanding, data-intensive, computing on earth with an enhanced core and chip architecture. It provides scalability and flexibility to handle changing customer needs while being cloud-ready with industry-leading reliability and performance.
The POWER9 Systems server family has servers that are available for different workloads, IT environments or budget. You can choose from an array of server options that include:
- POWER9 for Enterprise – scale-up
- POWER9 for AIX & IBM I – scale-out
- POWER9 for Linux
- POWER9 for SAP HANA
- POWER9 for Enterprise AI, Deep Learning & Machine Learning
Find out more about these servers to meet the business needs of tomorrow.
In the worlds of machine learning (ML) and deep learning (DL), operations and deployment is a subject that often falls by the wayside. In this 1-hour webinar, attendees discover what “AI Ops” looks like today, and where it’s going. Plus the sweet spot of ML/DL training workloads between data center and cloud.
Published By: Cylance
Published Date: Jul 02, 2018
The information security world is rich with information. From reviewing logs to analyzing malware, information is everywhere and in vast quantities, more than the workforce can cover. Artificial intelligence (AI) is a field of study that is adept at applying intelligence to vast amounts of data and deriving meaningful results. In this book, we will cover machine learning techniques in practical situations to improve your ability to thrive in a data driven world. With clustering, we will explore grouping items and identifying anomalies. With classification, we’ll cover how to train a model to distinguish between classes of inputs. In probability, we’ll answer the question “What are the odds?” and make use of the results. With deep learning, we’ll dive into the powerful biology inspired realms of AI that power some of the most effective methods in machine learning today. Learn more about AI in this eBook.
Published By: Cylance
Published Date: Jul 02, 2018
Artificial intelligence (AI) technologies are rapidly moving beyond the realms of academia and speculative fiction to enter the commercial mainstream, with innovative products that utilize AI transforming how we access and leverage information. AI is also becoming strategically important to national defense and in securing our critical financial, energy, intelligence, and communications infrastructures against state-sponsored cyberattacks. According to an October 2016 report issued by the federal government’s National Science and Technology Council Committee on Technology (NSTCC), “AI has important applications in cybersecurity, and is expected to play an increasing role for both defensive and offensive cyber measures.” Based on this projection, the NSTCC has issued a National Artificial Intelligence Research and Development Strategic Plan to guide federally-funded research and development. The era of AI has most definitely arrived, but many still don’t understand the basics of this im
Published By: Cylance
Published Date: Jul 02, 2018
The 21st century marks the rise of artificial intelligence (AI) and machine learning capabilities for mass consumption. A staggering surge of machine learning has been applied for myriad of uses — from self-driving cars to curing cancer. AI and machine learning have only recently entered the world of cybersecurity, but it’s occurring just in time. According to Gartner Research, the total market for all security will surpass $100B in 2019. Companies are looking to spend on innovation to secure against cyberthreats. As a result, more tech startups today tout AI to secure funding; and more established vendors now claim to embed machine learning in their products. Yet, the hype around AI and machine learning — what they are and how they work — has created confusion in the marketplace. How do you make sense of the claims? Can you test for yourself to know the truth? Cylance leads the cybersecurity world of AI. The company spearheaded an innovation revolution by replacing legacy antivirus software with predictive, preventative solutions and services that protect the endpoint — and the organization. Cylance stops zero-day threats and the most sophisticated known and unknown attacks. Read more in this analytical white paper.
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.
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.
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
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.
• 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.
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.”
"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"
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.
Published By: Genesys
Published Date: Jun 06, 2017
In this ebook, learn:
- Five trends will have the biggest impact on customer experience
- How to use machine learning to detect patterns and trends to deliver the next great customer experiences
- How to future-proof your contact center and adapt to changing customer needs
Every week InfoSight analyzes more than a trillion data points from
more than 9,000 customers. How does this translate into true
business value? By reducing your business risk with over Six-Nines
of measured availability. By providing you with an infrastructure
that gets “smarter” every single day. By empowering IT staff to
focus on business priorities instead of mundane maintenance.
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally.
Learn more about key findings, including:
Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics.
Return on investment for analytics stems from the governing and sharing of data throughout the organization.
Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
Published By: Pentaho
Published Date: Nov 04, 2015
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
What do these market-defining trends have in common?
· Analytics for all
· Analytics as competitive differentiator
· Internet of Things
· Artificial intelligence/Machine learning/Cognitive computing
· Real-time analytics/event management
They all rely on data – timely, accurate data delivered within an insightful context – to deliver value. The question is: who in the enterprise is most qualified and prepared to help deliver on the vision and values of the data-driven enterprise?
It’s going to take a special type of professional to deliver that value to enterprises. Organizations are seeking professionals to step forward and take the lead, provide guidance and lend expertise to move into the brave new world of digital. The move to digital and all that it entails – sophisticated data analytics, online customer engagement and digital process efficiency – requires, above all, the skills and knowledge associated with handling data and turning it into insights. The move to digital i
Learn how predictive analytics and machine learning can help optimize application performance and meet the needs of the business with Nimble Storage Infosight.
This ESG report highlights:
• Optimal application performance and delivery is difficult to achieve in complex environments.
• Many IT infrastructure and operations teams are stretched to the breaking point.
• Predictive analytics and machine learning can be applied to great effect.