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[slides] @Dyn's Cloud #APM and #NPM at @CloudExpo #AI #ML #Monitoring

#artificialintelligence

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. The upcoming 20th International @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA announces that its Call For Papers for speaking opportunities is open.


An Introduction to Machine Learning Theory and Its Applications

#artificialintelligence

And more recently, in 1997, Tom Mitchell gave a "well-posed" definition that has proven more useful to engineering types: "A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E." So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E) and, if it has successfully "learned", it will then do better at predicting future traffic patterns (performance measure P). On this flat screen we can draw you a picture of, at most, a three-dimensional data set, but ML problems commonly deal with data with millions of dimensions, and very complex predictor functions. The goal of ML is never to make "perfect" guesses, because ML deals in domains where there is no such thing. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data.


"Printing Money" with Operational Machine Learning

#artificialintelligence

However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. It includes machine learning models to customize offers, an open-source solution for run-time decisioning, and a scoring service to match customers and offers. In order to help create these capabilities, the company created both a Chief Data Officer and a Chief Loyalty and Analytics Officer within the marketing function. Building these capabilities on top of a big data stack (including data lake storage and data transformation capabilities) is transformational in terms of the availability of information to support the decision, the performance of the decision request, and the performance of the learning service.


How Data Science Will Change IT Operations

#artificialintelligence

However, despite the increase in instrumentation capabilities and the amount of collected data, the enterprises barely use significantly larger data sets to improve availability and performance process effectiveness with root cause analysis and incident prediction. The main task is to correlate events, tickets, alerts, and changes using cause-effect relationships, for example, linking a change request to the actual changes in the environment, linking an APM alert to a specific environment, and linking a log error to a particular web service. Machine learning can also be leveraged to build an environment dependency model based on environment topology, component dependencies, and configuration dependencies. Previously, it would take VSE's IT team weeks to investigate and resolve an incident, but by using analytics technologies, problems are traced and resolved in minutes, significantly increasing system availability and improving performance.


November Product Updates: Testing Our Way To 2017

Forbes

We've been running A/B tests on a small percentage of the mobile audience; testing new commenting and site socialization features, variations on UX treatments and relevancy matching on ad units, as well as some improvements aimed at streamlining page flow and better surfacing of related content. In the coming weeks, we'll be introducing improvements to how the CMS handles media, providing a simple, cohesive experience when adding media, and offering more granular search options. In November, ForbesConnect published the Forbes Healthcare app, a designated conference app for the Forbes Healthcare Summit in New York City. In December, we will continue our efforts to expand our business development plan in order to provide a light-weight networking platform for business schools.


[slides] #Monitoring with #AI @CloudExpo @Dynatrace #ML #IoT #DL #DigitalTransformation

#artificialintelligence

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. The upcoming 20th International @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA announces that its Call For Papers for speaking opportunities is open.


[slides] Monitoring with Artificial Intelligence @CloudExpo #AI #ML #IoT #BigData

#artificialintelligence

With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY. Join @CloudExpo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017 in New York City, for three days of intense'Internet of Things' discussion and focus, including Digital Transformation, Big Data's indispensable role in IoT, Smart Grids and Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) IoT's use in Vertical Markets. The company's internationally recognized brands include among others Cloud Expo (@CloudExpo), Big Data Expo (@BigDataExpo), DevOps Summit (@DevOpsSummit), @ThingsExpo (@ThingsExpo), Containers Expo (@ContainersExpo) and Microservices Expo (@MicroservicesE). Cloud Expo, Big Data Expo and @ThingsExpo are registered trademarks of Cloud Expo, Inc., a SYS-CON Events company.


How to Make Your Company Machine Learning Ready

#artificialintelligence

When people talk about AI, machine learning, automation, big data, cognitive computing, or deep learning, they're talking about the ability of machines to learn to fulfill objectives based on data and reasoning. In retail we will see more sophisticated supply chains, a deeper understanding of consumer preferences, and the ability to customize products and purchase experiences both on- and off-line. In agriculture, data will be used to decide which crops to grow, in what quantities, in what locations, and will render the growing process more efficient year after year. Fourth, if a process is complicated, use machine learning to create decision support systems.


Machine Learning in Education Opens a World of New Possibilities

#artificialintelligence

Machine learning focuses on developing computer programs that can teach themselves to grow and update the program embedded in a device by analyzing incoming data. Teaching and learning process enabled through machine learning also helps to improve the knowledge base of an individual. For instance, to enable students develop curiosity in pursuing the machine learning while improving the quality of education, IBM and its education partners are augmenting the education system by integrating machine learning. By building innovative digital classrooms, aka smart classroom, advancement in quality of education is being pursued to make the learning process engaging.


Tech Leaders Unite to Enable New Cloud Datacenter Server Designs for Big Data, Machine Learning, Analytics, and Other Emerging Workloads

#artificialintelligence

SAN JOSE, CA--(Marketwired - Oct 14, 2016) - Technology leaders AMD, Dell EMC, Google, Hewlett Packard Enterprise, IBM, Mellanox Technologies, Micron, NVIDIA and Xilinx today announced a new, open specification that can increase datacenter server performance by up to 10x, enabling corporate and cloud data centers to speed up big data, machine learning, analytics, and other emerging workloads. AMD "AMD is supporting OpenCAPI to bring high-performance accelerators from the Radeon Technologies Group into the datacenter, consistent with our work to establish open standards for accelerators that work across multiple processor architectures and suppliers," said Greg Stoner, AMD senior director, Radeon Open Compute. "Open standards and the open collaborations between companies and organization is key to develop the needed technology for the next generation cloud, Web 2.0, high performance, machine learning, big data, storage and more infrastructures." Xilinx "Xilinx is fully committed to bring high performance accelerators to market," said Gaurav Singh, vice president of Architecture at Xilinx.