The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and the need to rework faulty products.
Watch this webinar to learn how TIBCO’s Smart Manufacturing solutions can help you overcome these challenges. You will also see a demonstration of TIBCO technology in action around improving yield and optimizing processes while also saving costs.
What You Will Learn:
Applying advanced analytics & machine learning / AI techniques to optimize complex manufacturing processes
How multi-variate statistical process control can help to detect deviations from a baseline
How to monitor in real time the OEE and produce a 360 view of your factory
The webinar also highlights customer case studies from our clients who have already successfully implemented process optimization models.
Published By: ServiceNow
Published Date: May 14, 2019
Learn how ServiceNow has been applying machine learning and analytics with AIOps to help you cut through event noise to create actionable signals, identify service outages and degradations, remediate service and infrastructure issues accurately, and drive continuous improvement in service quality
NICE has made a significant investment into AI and ML techniques that are embedded into its core workforce management solution, NICE WFM. Recent advancements include learning models that find hidden patterns in the historical data used to generate forecasts for volume and work time. NICE WFM also has an AI tool that determines, from a series of more than 40 models, which single model will produce the best results for each work type being forecasted. NICE has also included machine learning in its scheduling processes which are discussed at length in the white paper.
Published By: IBM APAC
Published Date: May 14, 2019
Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future.
You will find topics like:
- What is machine learning?
- Explaining the business imperative
- The key machine learning algorithms
- Skills for your data science team
- How businesses are using machine learning
- The future of machine learning
Data is the lifeblood of business. And in the era of digital business,
the organizations that utilize data most effectively are also the most
successful. Whether structured, unstructured or semi-structured,
rapidly increasing data quantities must be brought into organizations,
stored and put to work to enable business strategies. Data integration
tools play a critical role in extracting data from a variety of sources and
making it available for enterprise applications, business intelligence
(BI), machine learning (ML) and other purposes. Many organization
seek to enhance the value of data for line-of-business managers by
enabling self-service access. This is increasingly important as large
volumes of unstructured data from Internet-of-Things (IOT) devices
are presenting organizations with opportunities for game-changing
insights from big data analytics. A new survey of 369 IT professionals,
from managers to directors and VPs of IT, by BizTechInsights on
behalf of IBM reveals the challe
Published By: CheckMarx
Published Date: Apr 03, 2019
Artificial Intelligence (AI) software is everywhere being leveraged by many industries such as healthcare, fintech, and e-commerce. But how does AI impact the security space? Join Maty Siman, Checkmarx Founder and CTO, to get both a white hat and black hat perspective to AI and security.
IBM Cloud Private for Data is an
integrated data science, data engineering
and app building platform built on top of
IBM Cloud Private (ICP). The latter is intended
to a) provide all the benefits of cloud
computing but inside your firewall and b)
provide a stepping-stone, should you want
one, to broader (public) cloud deployments.
Further, ICP has a micro-services architecture,
which has additional benefits, which we
will discuss. Going beyond this, ICP for Data
itself is intended to provide an environment
that will make it easier to implement datadriven processes and operations and, more
particularly, to support both the development
of AI and machine learning capabilities, and
their deployment. This last point is important
because there can easily be a disconnect
between data scientists (who often work for
business departments) and the people (usually
IT) who need to operationalise the work of
those data scientists
This year, businesses will increasingly turn to AI to power their business transformation. Machine learning and deep learning workloads are quickly becoming a mission-critical workload in the enterprise data center. As an IT leader, are you ready for the impending wave of AI applications that will demand new approaches to computing, storage and networking? Do you have the right strategy for scaling AI workload in your data center? We’ll introduce you to the IT visionaries who have made it happen. In this webinar we’ll explore how one IT leader accelerated his company’s success with an AI infrastructure strategy, sharing their best practices and insights.
By watching this webinar, you'll learn:
- Why it’s now critical for enterprise IT to have an AI infrastructure strategy that supports the business
- Explore one IT leader’s experience developing and implementing a best-of-breed platform for scaling AI workload in the data center
- Gain insights that can drive your AI infrastructur
Botnets are based on similar principles as legitimate clouds, but serve malicious business interests. Find out more about how botnets work and the right steps after having detected infected machines within your own network.
Artificial intelligence (AI) and machine learning (ML) are emerging technologies that will transform organizations faster than ever before. In the digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Historically, these insights were discovered through manually intensive data analytics—but the amount of data continues to grow, as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.
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
The Industrial Machinery industry is changing slower than it ever will and faster than it ever has. And customer demands are evolving at speeds never seen before. For companies serious about innovating at scale and transforming their business in order to dominate their market, it will take innovative thinking, disruptive technology and near flawless execution. This challenge, perhaps best described as the perfect blend of art and science, is more than achievable, but only if you have the right partner. Which is why we want you to meet Leonardo, by SAP. SAP Leonardo is a digital innovation system that enables organizations of all sizes to transform at scale with minimal risk and disruption. SAP Leonardo brings new technologies and services together to help businesses power their digital transformation. SAP Leonardo proves that truly transformative and sustainable innovation happens when technology, people, and data are combined.
The bar for success is rising in higher education. University leaders and IT administrators are aware of the compelling benefits of digital transformation overall—and artificial intelligence (AI) in particular. AI can amplify human capabilities by using machine learning, or deep learning, to convert the fast-growing and plentiful sources of data about all aspects of a university into actionable insights that drive better decisions. But when planning a transformational strategy, these leaders must prioritize operational continuity. It’s critical to protect the everyday activities of learning, research, and administration that rely on the IT infrastructure to consistently deliver data to its applications.
Artificial Intelligence (AI) has already begun to improve targeting, segmentation, media buying and planning in the advertising industry. AI algorithms can extract complex patterns from vast numbers of data points, and in so doing, are able to self-correct and learn patterns. The revenue potential that improved personalization, segmentation and targeting that AI provides to marketers is huge.
At HERE Technologies, we are placing AI and machine learning at the center of our products and services. We see the opportunity in automated machine learning to enrich the targeting and effectiveness of mobile advertising campaigns in real time. But the outcome of implementing such technology depends on the quality of data being fed into it from the outset. AI wouldn’t be as helpful if it’s being used alongside questionable location data or audience data.
HERE’s location data provides a strong thread that can be woven throughout every stage of the media buying process, offering more context and
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.
• 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.
The performance of enterprise applications will have a direct impact on business activities and outcomes. The quality of the delivery of applications will depend on how smoothly the underlying data infrastructure operates.
? 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
The increasing demands of application and database workloads, growing numbers of virtual machines, and more powerful processors are driving demand for ever-faster storage systems. Increasingly, IT organizations are turning to solid-state storage to meet these demands, with hybrid and all-flash arrays taking the place of traditional disk storage for high performance workloads.
Download this white paper to learn how you can get the most from your storage environment.
Today’s idea-driven economy calls for a simpler, faster virtualization solution—one that can be managed by one IT generalist vs. numerous IT specialists. Enter HPE Hyper Converged 380, an advanced, virtualized system from Hewlett Packard Enterprise. Based on the HPE ProLiant DL380 Gen9 Server, this enterprise-grade VM vending machine enables you to quickly deploy VMs, simplify IT operations, and reduce overall costs like no other hyperconverged system available today.
IoT has proven its value in the private sector. Ever since the 1980’s, US manufacturing has undergone a dramatic transition based on IoT. Machines that where once manually calibrated and maintained began to be controlled by specialized computers. These computers were able to quickly recalibrate tools which allowed manufactures to produce smaller batches of parts, but were also often locked into proprietary computing languages and architectures.
Modern storage arrays can’t compete on price without a range of data reduction
technologies that help reduce the overall total cost of ownership of external
storage. Unfortunately, there is no one single data reduction technology that fits
all data types and we see savings being made with both data deduplication and
compression, depending on the workload. Typically, OLTP-type data (databases)
work well with compression and can achieve between 2:1 and 3:1 reduction,
depending on the data itself. Deduplication works well with large volumes of
repeated data like virtual machines or virtual desktops, where many instances or
images are based off a similar “gold” master.
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: Cisco EMEA
Published Date: Nov 08, 2018
The goal of ESG Lab reports is to educate IT professionals about data center technology products for companies of all types and sizes.
ESG Lab reports are not meant to replace the evaluation process that should be conducted before making purchasing decisions, but rather to provide insight into these emerging technologies. Our objective is to go over some of the more valuable feature/functions of products, show how they can be used to solve real customer problems and identify any areas needing improvement.
ESG Lab's expert third party perspective is based on our own hands on testing as well as on interviews with customers who use these products in production environments.
Credit Union Times is the nation's leading independent source for breaking news and analysis for credit union leaders. For more than 20 years, Credit Union Times has set the standard for editorial excellence and ethical, straight-forward reporting.