Red Hat JBoss Enterprise Application Platform 7 Beta – Features That Will Excite You


Red Hat JBoss EAP 7 is a middleware platform which was built on open standards and compliant with Java Enterprise Edition 7 specifications. Red Hat JBoss Administrationbeta brings containers and micro-services closer to Java EE application server. Plus, it integrates the WildFly Application Server 10 with high availability messaging, clustering, distributed caching and other technologies.

Summary of New Features:

JBoss Enterprise Application Platform 7 – Everyone gets what they need to become more efficient and productive. Whether you are an inexperienced developer, a veteran Java EE expert or a staff member on testing, operations or administration teams. JBoss EAP 7 provides you an extra edge needed to deploy, build and maintain robust, high impact and web scale applications at DevOps speeds.

Optimized for Cloud Deployments and Container:

JBoss is built for containers and resource conscious virtualized or Cloud environments like OpenShift by Red Hat. JBoss EAP 7 provides a new designed web subsystem, Undertow uses the latest non blocking I/O features of Java to give improved performance and scalability. It also supports the latest Web Standards likeWebSockets, HTTP Upgrade and HTTP/2.

Supports Java SE 8 and Java EE 7:

JBoss Enterprise Application Platform 7 implements Java EE 7 Application Server and supports Java SE 8. JBoss requires Java SE 8 and EAP 7 has been tested with Open JDK 8 and Oracle JDK 8. Java EE 7 includes Batch 1.0, Concurrency 1.0, JSON-P 1.0, WebSocket 1.1, JPA 2.1, JMS 2.0, JCA 1.7 and many more.

Highly Scalable Web Server (Undertow):

The servlet and web server implementation has changed from JBoss web to Undertow. The word Undertow is a modern new I/O web server is designed for maximum scalability and throughput, including environments with more than million connections. Undertow supports non blocking handlers, asynchronous and traditional servlets and JSR 356 web socket handlers.

Strengthens Management:

Red Hat JBoss administration enhances the management command line interface functionality. The administrators can see the history of configuration changes made by authorized administrators.

Logging management has enhanced greatly, including the capacity to view the available log files on server, explain custom formatters for logs in the management comfort and configure the logging at the subsystem level in the management command line interface. The administrators can perform minimum maintenance on JBoss EAP 7 servers. They use new server – suspend mode to shut down servers only after completing inflight transactions.

DataSources and CA:

Enterprise Application Platform 7 supports distributed work manager implementation based on JGroups projects and JCA connection manager has strengthened with the lazy connection manger extensions. The resource adapters and data source subsystems provide setting capacity policies and plus flush strategies.

Compatibility:

JMS, EJB and WS customers would be compatible in every minor and patch releases of JBoss EAP 7. It manages domain administrator. It can also manage JBoss EAP 6 domain servers and hosts. Its domain controller can cope with other JBoss EAP 7 hosts in a managed domain. As long your domain controller is running on same or a recent version than the rest of the domain.

Interoperability:

JBoss EAP 7 is now available to have interoperability of remote EJB invocations between JBoss EAP 6 orJBoss EAP 5 on one side and JBoss EAP 7 on the other side. To make use of this feature, you should run the latest version of JBoss EAP 6 or JBoss EAP 5.

Advantages Offered by the Best Live Chat Agent Training Solutions


As a business manager, you must ensure that you always make the smartest decisions for your company, especially when it comes to improving your brand’s relationship with your target audience. Well, when visitors take a stroll on your website and get in touch with one of your chat agents, they should feel they had a positive experience regardless of nature of the assistance that they require. Good chat agent performance comes from good chat agent training. The good news is that Venice, CA boasts one of the best Live Chat Agent Training programs around today. To be the best, they use Machine Learning and Artificial Intelligence to create the best live chat agent training solutions.

While there are others who offer chat agent training in Venice, CA, there is only one that offers the most advanced Machining Learning Engineers. In fact, it is all a matter of contacting a team of artificial intelligence experts that focus on solutions for the enterprises. More specifically, you should be interested in chatting with someone that can provide live chat agent training solutions that address your specific needs all the way down to the individual chat agent. After all, the point of all this is to give you an important advantage that lets your company improve results when dealing with existing and prospective customers.

As you may know, new customers visiting a website usually only need a small nudge to make that final purchase. In most cases, a bit of assistance for a chat agent will make all the difference in the world. However, the agent must know exactly what to say and how to say it to get the expected reaction from the visitor. Here is where a Machine Learning technology coupled with clever AI feedback technology comes in.

The team can come up with live chat agent training solutions that specifically meet your needs and do not require you to hire additional staff. Instead, you would be investing in a product that uses the most advanced artificial intelligence available and can be implemented without disrupting the chat system that your team utilizes today. While talking to customers, the agents will have access to a friendly interface that offers recommendations, feedback and even an overall score of their performance.

Another incredible advantage associated with an artificial intelligence business solution is the fact that it will definitely improve your sales numbers by contributing to a more efficient communication approach used by your agents. From now on, whenever they talk to one or more of the website visitors, they will be able to come up with suitable answers that are scientifically more assured to result in a high level of customer satisfaction. Satisfied visitors are more inclined to place an order or make a purchase and all it takes is the help of an optimized AI system!

Are you still curious about any other advantages you could benefit from if you decided to invest in live chat agent training solutions in the near future? If that is the case, you might want to get in touch with a Machine Learning Engineer in Venice, CA or anyone from our artificial intelligence development team and talk about your needs! We are happy to help!

IaaS vs. PaaS vs. SaaS


IaaS vs. PaaS vs. SaaS

Cloud services are typically offered in three variations:
Infrastructure as a Service, Platform as a Service, or Software as a Service. A
business can utilize these individually or as a hybrid combination. The model that works best for you will be dictated by your goals and requirements. The common thread in each service is outsourcing your in-house load of responsibility. When you utilize the cloud, you lift the burden of managing your own software.

Tips for a Successful Cloud Migration


Tips for a Successful Cloud Migration

Cloud computing offers many potential benefits for SMBs. This is the story of Bob, an IT manager with his hands full. Bob needs a cloud migration strategy for his company, but there are important questions and challenges ahead, including finding the right provider who will partner with him on a plan and customized cloud solution.

PRIVATE CLOUD COMPUTING AND INFRASTRUCTURE AS A SERVIVE


Private cloud computing refers to a cloud infrastructure that is internal to an organization and is usually not available to the general public. A private cloud infrastructure is normally operated and managed by the organization that owns it. However, an organization can also get a public cloud provider to build, operate, and manage a private cloud infrastructure for an organization. Sometimes the operation and/or management of a private cloud infrastructure can also be outsourced to a 3rd party. A private cloud’s data centres can be on premise or off premise (for security and performance reasons).

Setting up a private cloud has a steep learning curve for the whole organization, for both users and administrators. Although highly scalable, with possibility for installing at a number of sites, it seems that the work needed to install and maintain large stage deployments is too big, and significant manpower would be needed, both in terms of time and skills. There is a need of more than average level of skills required for installing, managing, and using a private cloud. Moreover, private cloud needs current hardware. To mitigate all the work for manual firing up the instances, or services installed on those instances, an independent scripting language could be implemented for the purpose of automation.

Infrastructure as a Service (IaaS) delivers a computer infrastructure that is a fundamental resource like processing power, storage capacity and network to customers; instead of building data centers, purchasing servers, software or network equipments, a customer buys the resources as a fully outsourced service; a customer does not manage the underlying infrastructure but has full control over the operating systems and the applications running on it. IaaS models often provide automatic support for on demand scalability of computing and storage resources. It represents a new consumption model for the use of IT resources.

An IaaS provider offers customers bandwidth, storage and compute power on an elastic, on-demand basis, over the Internet. Companies reason for choosing an IaaS environment differ, depending on the size of the organization and the nature of the business. Cost is often the primary reason. IaaS other key benefits include improved cashflow, accommodation of widely inaccurate provision planning, and exceptional transparency in utilization and costs. Tata Communications has demonstrated its commitment to IaaS with the introduction of Insta Compute. Joining our suite of managed services that also includes collocation and managed hosting, Insta Compute adds a key component to the company’s IT services road map.

S Kirti is a web enthusiast and a writer. Kirti has afforded her articles and write-ups autonomously and through various online forums.
Get more information on: Private cloud computing & Infrastructure as a service

Building Blocks of MBaaS


Building Blocks of MBaaS

Basically, MBaaS with it’s cloud computing makes it easier for the developers to setup, use and operate a cloud backend for their mobile, tablet and web apps. MBaaS.

A report by Gartner says that 40% of mobile app development projects would make use of cloud-based mobile back-end services in 2016. With mobile back-end as a service, also known as MBaaS, 70% of the time dedicated to backend development can be reduced and more focus be given to frontend development.

In this video, Rajnish Taneja – AVP- Mobility Services at TO THE NEW Digital walks you through the building blocks of MBaaS including features that solutions provide. These includes user management, content management system (CMS), API, Storage and Push. While the user management focuses on user engagement through signups, the CMS that the MBaaS solution providers provide it allows to create, manage and delete with mobile adapted approach.

With digital space expanding, one of the key features is push notifications. Most solutions provide easy integration and segmented notifications.

Learn more about MBaaS by checking out our detailed webinar – http://bit.ly/1VcRb0k

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Apttus Customer Success – Aesynt


Apttus Customer Success – Aesynt

Read the full story of Aesynt as an Apttus customer here: http://apttus.com/aesynts-pharmacy-automation-solutions-rise-to-new-heights-after-choosing-apttus-cpq/

Here at Apttus, we value customer success, as well as the uplifting experiences our customers have.

Aesynt, a leading provider of integrated pharmacy automation solutions for hospitals and health systems, help hospitals and health systems support improved patient outcomes, build stronger businesses, and manage ongoing change. A customer-focused, innovative organization, they provide hospitals and health systems across the U.S. and Canada with high-quality, cost effective and efficient solutions that safely deliver the right medication closest to the patient.

Aesynt is a true Apttus customer success story after choosing Apttus CPQ to replace on-site legacy systems that relied heavily on Excel and email.

After Aesynt adopted Apttus CPQ, they were able to gain visibility on all of their deals, improve communication, produce accurate long-term projections, and increase speed and efficiency throughout their entire sales organization.

This customer video highlights how Aesynt was able to:
– Increase their deal size by a substantial amount
– Achieve a high rate of user adoption
– Manage pricing more effectively
– Better engage their customers and Improve their overall business

For more information on Apttus CPQ, visit http://apttus.com/solutions/cpq-software-solution/

For more information on Apttus customer success, visit http://apttus.com/customers-overview/customers/

Latest 70-346 Office 365 study materials


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PB-Scale AI Big Data Cloud Boot Camp – Build/Operate Deep Learning Data Pipeline/Lake Cloud/Container Cluster


PB-Scale AI Big Data Cloud Boot Camp – Build/Operate Deep Learning Data Pipeline/Lake Cloud/Container Cluster
Build & Operate Deep Learning Data Pipeline & Data Lake Cloud/Container Cluster with TensorFlow, Spark & Hadoop in GUI/AP/CLI Follow our Real-time AI Big Data Cloud Thinktank @ClouDatAI Boot Camp Overview Slides Fog Computing/Cloud Computing, Serverless Computing/Cloud-Native Computing, BlockChain/Bitcoin, Lambda Architecture, Microservices-oriented Architecture/monolithic architecture, Immutable Datalake, Real-time Data Pipeline, Container/VM/Bare Metal, IaaS/PaaS/SaaS, Machine Learning/Deep Learning, Supervised Learning/Unsupervised Learning, Big Data/Deep Learning, Hadoop/Spark, YARN/Mesos, Docker Engine/Kubernetes, OpenStack, SQL/NoSQL/HDFS, GUI/CLI/API, Hyper-scale/Hyper-convergence, SDN/NFV, GPU/CPU/TPU, File Storage/Object Storage/Block Storage, and much more. So are you feeling you are lost in the jungle of fast-pacing tech frontier? We Are Here to Help You to Get Out of It and Lead instead of Follow It! You go to a lot of trainings and/or meetups, whether free or not, expensive or cheap, ALL of those are either marketing fluff, sales pitches, or short of global pictures, or short of details, no insight, let alone foresight. Our 2-day Boot Camp is radically different, vendor agnostic, no strings attached, full of meat, lots of hands-on, offering you both macro & micro perspective of the state-of-the-art in practical way with hindsight, insight and foresight! What you'll learn, and how you can apply it Learn how  Machine & Deep Learning AI Big Data Container enables data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities Topics include: How to identify potential business use cases in leveraging big data container AI technology How to obtain, clean, and combine disparate data sources to create a data pipeline for data lake What Machine-Learning (Shallow Learning) & Deep Learning technique to use for a particular data science project How to conduct PoC & productionalized big data projects in cloud/container cluster at scale Understand & master TensorFlow's fundamentals & capabilities Explore TensorBoard to debug and optimize your own Neural Network Architectures, train, test, validate & serve your models for real-life Deep Learning applications at Scale Agenda (Subject to Change at Anytime without Notice) – 50% Lecture, 50% Hands-On, Vendor Agnostic, No Strings Attached, You Working on a Cluster instead of only an Instance in cloud/container, True PB-Scale Depends on Your Own Cloud Budget (could be outstanding) as opposed to Free Trial Limited Budget, whether pick Cloud-based topic or Container-based topic, soly depends on Instructor's decision onsite Day 1 10:00 AM – 10:50AM Elastic Cloud Computing and Scalabe Big Data AI: What, Why and How? 11:00 AM – 11:50AM Deep Dive into Public/Private/Hybrid Cloud Infrastructure: Elastic/Plastic Cloud; Bare Metal/VM/Container; IaaS/PaaS/SaaS; Hyper-Scale/Hyper-Convergence; From Linux Kernel to Distributed System's CAP Theorem; OpenStack as the De facto Private Cloud; Capacity Planning & Auto-scaling Challenges of Cloud; Micro-service-based Immutable Architecture 12:00 AM – 12:50AM Deep Dive into Big Data Technology Stack: Nature of Big Data – Structural/Unstructural; Hot/Warm/Cold; Machine/Human; Text/Numerical, SQL(ACID)/NoSQL(BASE); Batch(Hindsight)/Interactive (Insight)/Streaming(Foresight); Data Pipeline & Datalake; Hadoop/Spark/Kafka/HDFS/HBase/HIVE/ZooKeeper 1:00 PM – 1:50M Lunch Session (Lunch included, Veggie option available): Google/AWS Cloud|Docker/CoreOS Container In-Depth: Computation/Storage/Networking Models 2:00PM – 6PM Hands-on I: I Set Up & Test Drive Your Own AI Big Data Google/AWS Cloud|Docker/CoreOS Container Cluster (Hadoop, Spark, Kafka, HDFS, Tensorflow) : Using Spark/Hadoop for Word Counting of Twitter Data/KNN-based Recommendation System Building, all based on Big DataDay 2 10:00 AM – 10:50AM Practical Machine Learning In-Depth: Feature Engineering, From Regression to Classification, 5 Tribes of Machine Learning: Symbolists with Inverse Deduction of Symbolic Logic, Connectionists with Backpropagation of Neural Networks, Evolutionaries with Genetic Programming, Bayesians with Probabilistic Inference in Statistics, Analogizers with Support Vector Machines; Supervised Learning (Classification/Regression), Unsupervised Learning (Clustering), Semi-Supervised Learning; Data Ingestion & Its Challenges, Data Cleansing/Prep-processing; Training Set/Testing Set Partitioning; Feature Engineering (Feature Extraction/Selection/Construction/Learning, Dimension Reduction); Model Building/Evaluation/Deployment|Serving/Scaling|Reduction/Optimization with Prediction Feedbacks 11:00 AM – 11:50AM Practical Deep-Learning-based AI In-Depth: Weak/Special AI vs Strong/General AI; Key Components of AI: Knowledge Representation, Deduction, Reasoning, NLP, Planning, Learning,Perception, Sensing & Actuation, Goals & Problem Solving, Consciousness & Creativity; Rectangle of Deep Learning, Shallow Learning, Supervised Learning, and Unsupervised Learning; Basic Multi-layer Architecture of Deep Forward/Convolutional Neural Networks(FNN/CNN)/Deep Recurrent Neural Networks(RNN)/Long short-term memory(LSTM): Input/Hidden/Output Layers, Weights, Biases, Activation Function, Feedback Loops, Backpropagation from Automatic Differentiation and Stochastic Gradient Descent (SGD); Convex/Non-Convex Optimization; Ways of Training Deep Neural Networks: Data/Model Parallelism, Synchronous/Asynchronous Training, Variants of SGD, Gradient Vanishing/Explotion, Loss Function Minimization/Optimization with Dropout/Regulariztion & Batch Normalization & Learning Rate & Training Steps, and Unsupervised Pre-training (Autoencoder etc.); Deep Learning Applications – What's Fit and What's Not?: Deep Structures, Unusual RNN, Huge Models 12:00 AM – 12:50PM Embracing Paradigm Shifting from Algorithm-based Rigid Computing to Model-based Big Data Cloud IoT-powered Deep Learning AI for Real-Life Problem Solving: What, Why and How? – Problem Formulation, Data Gathering, Algorithmic & Neural Network Architecture Selection, Hyperparameter Turning, Deep Learning, Cross Validation, and Model Serving 1:00 PM – 1:50PM Lunch Session (Lunch included, Veggie option available) – Tensorflow In-Depth: The Origin, Fundamental Concepts (Tensors/Data Flow Graph & More), Historical Development & Theoretical Foundation; Two Major Deep Learning Models and Their TensorFlow Implementation: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN); GPU/Tensorflow vs. CPU/NumPy; TensorFlow vs Other Open Source Deep Learning Packages: Torch, Caffe, MXNet, Theano: Programming vs. Configuration; Tackling Deep Learning Blackbox Puzzle with TensorBoard 2:00PM – 6PM Hands-on I Continued: I Set Up & Test Drive Your Own AI Big Data Google/AWS Cloud|Docker/CoreOS Container Cluster (Hadoop, Spark, Kafka, HDFS, Tensorflow) : Using Spark/Hadoop for Word Counting of Twitter Data/KNN-based Recommendation System Building, all based on Big Data                       Hands-on II (Only for Advanced Attendeeds):  Build, Train & Serve Your Own Chosen AI Application Using Python in Your Own Scalable AI Big Data Google/AWS Cloud|Docker/CoreOS Container Cluster (TensorFlow, Spark, Hadoop, Kafka, HBase, HIVE, Zookeeper) Who Should Attend: CEO, SVP/VP, C-Level, Director, Global Head, Manager, Decision-makers, Business Executives, Analysts, Project managers, Analytics managers, Data Scientist, Statistian, Sales, Marketing, human resources, Engineers, Developers, Architects, Networking specialists, Students, Professional Services, Data Analyst, BI Developer/Architect, QA, Performance Engineers, Data Warehouse Professional, Sales, Pre Sales, Technical Marketing, PM, Teaching Staff, Delivery Manager and other line-of-business executives Statisticians, Big Data Engineer, Data Scientists, Business Intelligence professionals, Teaching Staffs, Delivery Managers, Product Managers, Cloud Operaters, Devops, System admins, Business Analysts, Financial Analysts, Solution Architects, Pre-sales, Sales, Post-Sales, Marketers, Project Managers, and Big Data Cloud AI Enthusiasts.Hands-on Requirements:1) Each student should bring their own 64bit Linux-based or Windows with Putty installed laptop (no VM required as we are using cloud)  with administrative/root privileges and wireless connectivity; and with Minimum 8GB RAM & Free 0.5TB hard disk When Instructor decides to go Cloud track 2) Google/AWS Cloud account ready only When Instructor decides to go Cloud track 3) Docker/CoreOS Container pre-installed in your laptop only When Instructor decides to go Container track 4) It's better but not necessry to bring your own WiFi hotspot 5) Bash and Python are necessary but not required
starting on 2017-05-27 10:00:00

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IS DATA CENTER COOLING MARKET THE FUTURE?


As the world moves towards cloud and big data, demand for storage has increased manifold. Data Centers help in meeting the storage needs, but also comes with challenges. Data centers use 100 times more energy than a standard office building hence a properly designed cooling system is more essential to improve the energy efficiency of the data centers.
Data center cooling will play a major part in decision making process of enterprises who wish to use the services for their business operations. They have become a necessity in any organization with a significant IT infrastructure. To meet the requirements of the customers, IT organizations are deploying various new applications which are leading to space, power and cooling shortage. These issues have led many organizations to realize data center costs are now part of their limited budget. Cooling is an important aspect of data center management, as majority of the power consumption by data centers is used for cooling systems. Most datacenters store sensitive data and are liable to damage at high temperature making temperature control imperative. There are various methods for data center cooling. Some of the majorly used techniques are air conditioning, chillers, Economizers and submersion cooling.
As companies look to reduce costs, energy efficient methods of cooling are being looked at as a potential alternative to traditional cooling methods because the power used in cooling accounts for around 39 % of the total power used to maintain data centers. Data is a vital asset to companies and hence there are many opportunities for cooling centers in legacy data centers which still use outdated cooling equipment, and therefore, spend inordinately on cooling and emit large quantities of greenhouse gases.
Companies are also starting to outsource their IT management to dedicated and shared cloud offerings in service provider datacenters. Another significant development in datacenter is the growing importance of service provider mega datacenters, which are the primary server location for cloud service providers and they require state of the art cooling systems to be cost effective.
The rapid need for more data centers to house the growing server population due to the insatiable need for more business applications has increased cost and energy usage. With governments contemplating regulations on power consumptions, companies need to reduce energy consumption. After the recent financial crisis, companies are looking to optimize their spending on cooling as it cannot be totally removed. Newer cooling mechanisms consume far much lesser electricity and reduce the total cost of ownership for companies. Cooling systems with good tile placement and air flow modeling can reduce the power consumption by 50 %. Hence, there is good opportunity as companies are increasingly adopting newer cooling systems which are both cost-efficient and energy efficient.
With companies expanding, the need for data centers is rapidly growing and cooling systems also. Companies are accumulating huge amounts of data and are investing for more data centers to store such huge data. Data centers are also ensure business continuity, which is important in today world where companies are spread across different continents. Disaster Recovery is another important factor which drives companies to store data in data centers. In addition with the increase in adoption of desktop virtualization, cloud computing and Big data the need for data centers is enormously increasing which directly impacts the growth of the data center cooling market. The market of Data center cooling is filled with substantial opportunities and is expected to see lucrative growth in the coming decade.

Mordor Intelligence is a global market research and consulting firm.Our singular focus is to provide research insights for business success. Our research team has expertise in diverse fields like Agriculture | Healthcare | ICT | Chemicals | Manufacturing | Logistics | Electronics and Automotive.