machine learning

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By: Microsoft Azure     Published Date: Apr 10, 2018
Read why Forrester ranked Microsoft a leader in SFA Solutions You have bold ambitions for your sales team. You want to—and need to—reinvent productivity. But the success of your sales team is stifled by silos. It’s no wonder they’re struggling when their transactional systems, social networking, and productivity tools are separate. With the Microsoft Relationship Sales solution, you can scale the power of one-on-one relationship selling by unifying the sales experience. And you can help empower your sellers with savvy insights that engage and delight customers. It combines Dynamics 365 for Sales with LinkedIn Sales Navigator to help sellers identify the right customers—and the right time and way to engage with them. Read The Forrester Wave™: Sales Force Automation Solutions, Q2 2017 report that says Microsoft is a best fit "...for those companies that are bullish and looking to disrupt their peers with AI and machine learning." Empower your team to reinvent the way they sell—and he
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     Microsoft Azure
By: Cyphort     Published Date: Jun 28, 2016
Computer viruses have plagued personal computers since the original Brain virus began infecting boot sectors in 1986. Originally, these early viruses were annoying, but fundamentally benign in nature. However, once the initial concept of malicious propagating code became established, the actors creating viruses became more sophisticated in their approach. Ultimately, the results of a successful infection were more significant and the impact on an enterprise more severe.
Tags : technology, security, best practices, solutions, network management
     Cyphort
By: CrowdStrike     Published Date: Nov 28, 2018
While many organizations are guarding the front door with yesterday’s signature-based antivirus (AV) solutions, today’s unknown malware walks out the back door with all their data. What’s the answer? This white paper, “The Rise of Machine Learning in Cybersecurity,” explains machine learning (ML) technology — what it is, how it works and why it offers better protection against the sophisticated attacks that bypass standard security measures. You’ll also learn about CrowdStrike’s exclusive ML technology and how, as part of the Falcon platform’s next-gen AV solution, it dramatically increases your ability to detect attacks that use unknown malware. Download this white paper to learn: • How different types of ML are applied in various industries and why it’s such an effective tool against unknown malware • Why ML technologies differ and what factors can increase the accuracy and effectiveness of ML • How CrowdStrike’s ML-based technology works as part of the Falcon platform’s next-gene
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     CrowdStrike
By: McAfee     Published Date: Mar 31, 2017
Overwhelmed by the volume of security intelligence and alerts, human analysts need machine learning to augment and accelerate efforts. Machine learning moves security analytics from diagnostic and descriptive to prescriptive and proactive, leading to faster and more accurate detection.
Tags : machine learning, advanced analytics, advanced threats, sandbox, zero-day, malware, mcafee labs, dynamic endpoint
     McAfee
By: MarkLogic     Published Date: Nov 30, 2017
The OPDBMS market in 2017 brings cloud and fully managed options center stage for execution. Market-defining vision includes features for machine learning, serverless scenarios and streaming integration. Data and analytics leaders must balance current and future needs against this market landscape.
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     MarkLogic
By: BlackBerry Cylance     Published Date: Sep 13, 2017
" Artificial intelligence and machine learning approaches provide radically new and improved endpoint protection. But not all companies' claims of using machine learning add up to a threat prevention strategy. Know the difference."
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     BlackBerry Cylance
By: Gigaom     Published Date: Oct 24, 2019
A huge array of BI, analytics, data prep and machine learning platforms exist in the market, and each of those may have a variety of connectors to different databases, file systems and applications, both on-premises and in the cloud. But in today’s world of myriad data sources, simple connectivity is just table stakes. What’s essential is a data access strategy that accounts for the variety of data sources out there, including relational and NoSQL databases, file formats across storage systems — even enterprise SaaS applications — and can make them all consumable by tools and applications built for tabular data. In today’s data-driven business environment, fitting omni-structured data and disparate applications into a consistent data API makes comprehensive integration, and insights, achievable. Want to learn more and map out your data access strategy? Join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guests, Eric
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     Gigaom
By: AWS     Published Date: Jun 07, 2019
Watch this webinar to learn how Slalom helped Veripad enhance the accuracy of their machine learning models using AWS services. Together they helped health professionals detect fraudulent medications more accurately than the human eye.
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     AWS
By: AWS     Published Date: Jul 24, 2019
Many business leaders know that Artificial Intelligence (AI) and Machine Learning (ML) are critical to their future but don’t know where to start. Those who do have an AI/ML strategy struggle to find qualified data scientists; and once they find them, even advanced data scientists need a lot of time—even months—to build and deploy ML models. These challenges put significant limits on the range and number of problems a business can solve. In this webinar, learn how H2O Driverless AI on Amazon Web Services (AWS) automates the best practices of leading data scientists to create advanced machine learning models automatically. With these production-ready models, relative newcomers to AI/ML can generate reliable results and scale-up AI programs that anticipate and capitalize on trends, optimize supply chains, understand customer demand, match consumers with goods and services, and much more. Download our webinar to learn Implement ML successfully with minimal data science expertise. Build
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     AWS
By: AWS     Published Date: Jul 24, 2019
Common daily media broadcaster tasks such as ad verification are slow and costly. Done manually, they may also introduce inefficiencies that can interfere with transparency and payment accountability—and impact your bottom line. Meanwhile, recent and archived media lies idle when you could repurpose it to increase brand exposure and generate revenue. Learn how Veritone, Inc. used its aiWARE Operating System, building on Amazon Web Services (AWS), to help Westwood One, Inc., a large audio broadcasting network in the United States, develop Artificial Intelligence (AI) and Machine Learning (ML) solutions designed for ad verification and monetizing archived media. Download our webinar to learn how you can Automate ad verification and reporting tasks. Enhance archive content to make media searchable and reusable. Use AI and ML in the cloud for near real-time media intelligence. Start applying machine learning tools.
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     AWS
By: AWS     Published Date: Jul 24, 2019
Trupanion, a Seattle-based medical insurance provider for cats and dogs, needed to find data insights quickly. With only 1% of pet owners insured, the process of evaluating a claim to approve or deny payment was manual and time-consuming. Building accurate predictive models for decision-making required manpower, time, and technology that the small company simply did not have. DataRobot Cloud, built on AWS, helped Trupanion create an automated method for building data models using machine learning that reduced the time required to process claims from minutes to seconds. Join our webinar to hear how Trupanion transformed itself into an AI-driven organization, with robust data analysis and data science project prototyping that empowered the company to make better decisions and optimize business processes in less time and at a reduced cost. Join our webinar to learn: Why you don’t need to be an expert in data science to create accurate predictive models. How you can build and deploy pr
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     AWS
By: AWS     Published Date: Jul 24, 2019
Join us to learn why Human-in-the-Loop training data should be powering your machine learning (ML) projects and how to make it happen. If you’re curious about what human-in-the-loop machine learning actually looks like, join Figure Eight CTO Robert Munro and AWS machine learning experts to learn how to effectively incorporate active learning and human-in-the-loop practices in your ML projects to achieve better results. You'll learn: When to use human-in-the-loop as an effective strategy for machine learning projects How to set up an effective interface to get the most out of human intelligence How to ensure high-quality, accurate data sets When: Available On Demand (please register to view) Who Should Attend: IT leaders and professionals, line-of-business managers, business decision makers, data scientists, developers, and other experts interested in implementing AI/ML on the cloud are encouraged to attend this webinar. AWS Speaker: Chris Burns, Solutions Architect Figure Eight Spea
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     AWS
By: AWS     Published Date: Jun 24, 2019
Join our webinar to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS. Webinar attendees will learn: Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights. How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS. How easy it is to get started with machine learning solutions for data wrangling on the cloud.
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     AWS
By: Schneider Electric     Published Date: Aug 15, 2017
Schneider Electric is integrating datacenter infrastructure management (DCIM) software, big-data analytics and cloud services into the management of customers’ datacenters. Its recently launched StruxureOn cloud offering signals a new wave in datacenter operations, using a combination of machine learning, anomaly detection and event-stream playback to give operators real-time insights and alarming via their smartphones. More capabilities and features are planned, including predictive analysis and, eventually, automated action. Schneider’s long-term strategy is to build a partner ecosystem around StruxureOn, and provide digital services that span its traditional datacenter business.
Tags : incident tracking, historical trending, troubleshooting, operational analysis, prediction model, schneider equipment, maintenance, firmware updates
     Schneider Electric
By: BlackBerry 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.
Tags : artificial, intelligence, enterprise
     BlackBerry Cylance
By: BlackBerry 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
Tags : artificial, intelligence, cybersecurity, machine
     BlackBerry Cylance
By: BlackBerry 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.
Tags : cylance, endpoint, protection, cyber, security
     BlackBerry Cylance
By: BlackBerry Cylance     Published Date: Mar 12, 2019
A Pathfinder paper navigates decision-makers through the issues surrounding a specific technology or business case, explores the business value of adoption, and recommends the range of considerations and concrete next steps in the decision-making process.
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     BlackBerry Cylance
By: BlackBerry Cylance     Published Date: Apr 26, 2019
The concept of artificial intelligence (AI) has been with us since the term was coined for the Dartmouth Summer Research Project on Artificial Intelligence in 1956. Today, while general AI strives for full cognitive abilities, there is a narrower scope—this better-defined AI is the domain of machine learning (ML) and other algorithm-driven solutions where cybersecurity has embraced AI. SANS recently conducted a survey of professionals working or active in cybersecurity, and involved with or interested in the use of AI for improving the security posture of their organization. Read their report to learn their survey findings, conclusions, and recommended considerations.
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     BlackBerry Cylance
By: SAS     Published Date: Mar 06, 2018
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 of 2020. 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
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     SAS
By: SAS     Published Date: Mar 06, 2018
There is a lot of excitement in the market about artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Although many of these technologies have been available for decades, new advancements in compute power along with new algorithmic developments are making these technologies more attractive to early adopter companies. These organizations are embracing advanced analytics technologies for a number of reasons including improving operational efficiencies, better understanding behaviors, and gaining competitive advantage.
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     SAS
By: SAS     Published Date: Mar 06, 2018
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.
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     SAS
By: SAS     Published Date: Jun 06, 2018
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.
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     SAS
By: IBM     Published Date: Apr 07, 2017
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization. This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
Tags : data analytics, product refinement, business exploration, advanced prototyping, analytics, data preparation, customer support, sales relations
     IBM
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
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