Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
Predictive analytics is powerful. It can help drive significant improvement to an organization’s bottom line. Look for ways to use it to grow revenue, shrink costs and improve margins.
Provide a platform that enables your data scientists to work efficiently using tools and algorithms they prefer. Enhance your analyses with internal and external data, structured and unstructured data. Then make the analytics accessible in order to reap the full benefits of these valuable analyses.
Stay ahead of the curve in your market with predictive analytics, and give your organization a competitive advantage and an improved bottom line.
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
This self-paced workshop teaches an end-to-end computer vision workflow using the latest Intel® technologies and the Intel® Distribution of OpenVINO™ toolkit.
Learn how to:
Use deep learning algorithms that help accelerate smart video applications
Optimize and improve performance with and without external accelerators
Use tools to help you identify the best hardware configuration for your needs
Apply frameworks and topologies supported by the Intel® accelerator tool
Published By: Marketo
Published Date: Mar 22, 2018
Advertising on social media platforms has changed tremendously.
Recent updates to many social networks' algorithms give users
a better experience—one with less promotional content and
more relevant content that they want to see. This means that, as a
marketer, you need to supplement your organic posts with paid
promotion to get your posts seen by your audience. Download this eBook to learn more!
Published By: Microworld
Published Date: Dec 12, 2007
NILP from MicroWorld is an advanced, next generation technology that detects Spam and Phishing mails using unique algorithms. Before we see how NILP works, let’s first check out the magnitude of spam trouble and why traditional methods are failing to counter it.
Published By: MobileIron
Published Date: Aug 20, 2018
MobileIron knows that cybercriminals are continuously generating more advanced ways to steal your data by any means necessary. That’s why we are committed to continually innovating and delivering new solutions that help our customers win the race against time to get ahead of the latest mobile security threats. As part of that commitment, MobileIron Threat Defense supports the five critical steps to deploying advanced, on-device mobile security. Our solution provides a single, integrated app that delivers several key advantages:
• A single app of threat protection is fully integrated with EMM.
• No user action is required to activate or update on-device security.
• Advanced mobile security blocks known and zero-day threats across iOS and Android devices with no Internet connectivity required.
• Machine-learning algorithms instantly detect and remediate on-device DNA threats.
Published By: Monetate
Published Date: Oct 11, 2018
Monetate Intelligent Recommendations is the only solution that gives merchandisers & digital marketers the power to show contextually relevant product recommendations without burdening IT resources.
Using manually curated or algorithmically-driven recommendations, marketers can easily support even the most complex product catalogs. Our solution filters recommendations based on customer attributes (e.g. shirt size), longitudinal behaviours (e.g. browsing behaviour), and situational context (e.g. product inventory at local stores). Best of all, an orchestration layer intelligently selects which algorithms and which filters to apply in any given situation, for any particular individual.
Published By: Monetate
Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximise brand value, while still allowing the control of an unlimited number of business guardrails defined by you.
Published By: Monetate
Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations
at scale without sacrificing any of the control you require. Our
proprietary algorithms know what to serve each individual shopper to
maximize brand value, while still allowing the control of an unlimited
number of business guardrails that you define.
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloudand what it can help organisations achieve. Talking about innovation,
security and efficiency, they put the casefor an autonomous future.
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloud and what it can help organisations achieve. Talking about innovation,security and efficiency, they put the case for an autonomous future.
Watch the webinar.
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing.
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to analyze and action data using artificial intelligence (AI). Financial services companies can greatly benefit from this leap forward in technology that enables unique insights into all aspects of the industry. With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists now have the ability to tackle complex problems and find credible and profitable solutions.
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
In 2017, review sources proliferated, consumers became more savvy about the validity of online reviews, and the position of Chief Experience Officer started to gain traction among locationbased organizations. ORM and SEO became increasingly intertwined as Google refined its search algorithms with a strong emphasis on reviews and star ratings.
The past year ushered in some big changes for Online Reputation
Management (ORM) — and the practice has become indispensable for any
In 2017, review sources proliferated, consumers became more savvy about the validity of online
reviews, and the position of Chief Experience Officer started to gain traction among locationbased
organizations. ORM and SEO became increasingly intertwined as Google refined its search
algorithms with a strong emphasis on reviews and star ratings.
This year, expect to see these four trends move to the forefront:
1) Google will extend its dominance in online review volume and consumer exposure, eclipsing all
other specialty sites.
2) SEO will be reinvented as user-generated reviews weigh more heavily in search rankings.
3) The voice of the customer will no longer be siloed across disconnected categories.
4) Consumer feedback from reviews and social media will drive operational improvements.
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars.
This paper, based on presentations by SAS Data Scientist Wayne Thompson, introduces key machine learning concepts and describes SAS solutions that enable data scientists and other analytical professionals to perform machine learning at scale. It tells how a SAS customer is using digital images and machine learning techniques to reduce defects in the semiconductor manufacturing process.
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated
drive as you read the morning news. We are getting very close to that kind of
scenario, and companies like Ford expect to have production vehicles in the latter part
Driverless cars are just one popular example of machine learning. It’s also used in
countless applications such as predicting fraud, identifying terrorists, recommending
the right products to customers at the right time, and correctly identifying medical
symptoms to prescribe appropriate treatments.
The concept of machine learning has been around for decades. What’s new is that
it can now be applied to huge quantities of data. Cheaper data storage, distributed
processing, more powerful computers and new analytical opportunities have dramatically
increased interest in machine learning systems. Other reasons for the increased
momentum include: maturing capabilities with methods and algorithms refactored to
run in memory; the
Machines learn by studying data to detect patterns or by applying known rules to:
• Categorize or catalog like people or things
• Predict likely outcomes or actions based on identified patterns
• Identify hitherto unknown patterns and relationships
• Detect anomalous or unexpected behaviors
The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
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.
How can you open your analytics program to all
types of programming languages and all levels of
users? And how can you ensure consistency across
your models and your resulting actions no matter
where they initiate in the company?
With today’s analytics technologies, the conversation
about open analytics and commerical analytics is no
longer an either/or discussion. You can now combine
the benefits of SAS and open source analytics
technology systems within your organization.
As we think about the entire analytics life cycle, it’s
important to consider data preparation, deployment,
performance, scalability and governance, in addition
to algorithms. Within that cycle, there’s a role for
open source and commercial analytics.
For example, machine learning algorithms can
be developed in SAS or Python, then deployed in
real-time data streams within SAS Event Stream
Processing, while also integrating with open systems
through Java and C APIs, RESTful web services,
Apache Kafka, HDFS and more.
The intrepid data miner runs many risks, including being buried under mountains of data or disappearing along with the "mysterious disappearing terabyte." This article outlines some risks, debunks some myths, and attempts to provide some protective "hard hats" for data miners in the technology sector.
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.
Data Centre Dynamics Ltd.
102-108 Clifton Street
London EC2A 4HW