Corporate Training key to biz success

Corporate Training has begun to be recognised as a necessity in almost every business organisation today keeping in mind the benefits it brings. The need for it was felt after fast-paced changes began occurring in industry and businesses which in turn kept throwing up new technology challenges over the way tasks and processes were carried out. Employees needed to be reoriented to the changing scenario and that needed training and some re-skilling.



Changing tech scenario, plugging skill gaps
Recent studies suggest that companies that offer comprehensive training programs have 218% higher income per employee than companies that don’t prioritise it. And understandably, such companies also enjoy a 24% higher profit growth. A large part of employee training in driven by the need to familiarise staff with new technology that a firm has introduced or when new methods of doing stuff have been devised. The fast pace at which new technology is being introduced the world over needs to be quickly incorporated into the company’s processes with the aim of increasing efficiency. This is where training helps employees pick up new skills and/or sharpen existing.
Boosting bottom line
That apart, companies have also began to realise that investing in training and development of employees – their most valuable asset – improves their work ability and widens their potential, thereby boosting productivity.

Low attrition, high loyalty
Corporate training and development positively impacts employee output and behaviour in several ways. For one, it reduces the attrition rate in that the staff who have received on-job training are more likely to be loyal and stay with the company. Studies show that such staff also experience increased job satisfaction. Increase in employees’ motivation levels leads to higher productivity and enhanced profitability.

Profitable trade-off
By training employees, companies are actually optimising their own financial and other resources. The right kind of online or on-site training increases employees’ skills and knowledge, which allows spending less on supervision etc. The thing to learn here is -- investing time and money in employees pays dividends to both, the employer and employee, and strengthens their relationship.

AI : Popular Tools & Technologies

Artificial Intelligence

Popular Tools & Technologies

The objective of artificial intelligence is to develop machines that learn to perform tasks like humans and eventually do them better than humans. The tools used towards that end are designed to understand actions of humansand machines and replicate these, predict the next logical step, and improvise and improve the output. The approach developers commonly take to achieve this combine statistical methods and computational intelligence, while the tools they use include logic and methods based on probability,etc.

 

Among the popular AI tools are Tensorflow, PyTorch, Scikit-Learn, MicrosoftCNTK, Seaborn, and some others. Some of the popular ones are open-source and hence commonly used by developers. Let us find out what each of these offer.

 

TensorFlow

This is an open-source software library. The system, though initially developed by Google for is researchers and engineers working in its Machine Intelligence division, is general enough to be applied in a host of other domains. TensorFlowis used forperforming high-end numericalcomputations, deep neuralresearch, image recognition, voice and facial expression and natural language processing.

MicrosoftCNTK

Microsoft CNTK, also known as Microsoft Cognitive Toolkit, is a deep learning framework developed by Microsoft Research. It is an open-sourceand easytousetoolkitthattrainsdeeplearningalgorithmstolearnlikehuman brains. The toolkit allows distributedtraining, supports C, C++, Java andPython, the preferred languages of many developers.

 

Scikit-Learn

This too is an open-source Python library used for machine learning offering a range of algorithms such as Clustering, Regression and Classification. Scikit-Learn is prefered for machine learning and AI because it offers dimensionalityreduction, bundle of classificationalgorithms, unsupervised learning algorithms and clusteringalgorithms and efficient for datamining.

Keras

Keras is written in Python and is yet another open-source neural-

network library. It is capable of running on top of Tensorflow, Microsoft Cognitive Toolkit, R, Theano, etc. Keras Python also deals with Neural Networks. It offers consistent and simpleAPIs, minimises the number of user actions required for common use cases, and provides clear and actionable feedback upon usererror.

OpenCV

This is a cross-platform library which focuses mainly on image processing, video capture and analysis including features like face detection and object detection.

 

PyTorch

PyTorch has a production-ready Python library with excellent applications, demos and use cases. It includes a machine learning compiler called Glow that boosts the performance of deeplearningframeworks.

NumPy

This is another Python library that deals with complex mathematical operations like linear algebra, Fourier transformation, random number and features that work with matrices and n-dimensional arrays inPython.

Pandas

The Pandas library is built on top of Numpy, which means it needs the latter to operate. Pandas helps manage complex data operation with just one or two commands i.e. it serves as the best starting point to create a more focused and powerful data tools.

Matplotlib

Matplotlib is a visualization library written in Python for 2D plots of arrays. This too is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

 

Seaborn

Seaborn is unique and an exceptional visualization library. Based on Matplotlib foundations, it offers advanced-level dataset based interface to make high-quality statisticalgraphics.Seaborn’s features allow developers to perform statistical estimation when combining data across observations, plotting and visualizing the suitability of statistical models to strengthen datapatterns.

Artificial Intelligence

AI making Rapid Strides

With its immense benefits becoming obvious, artificial intelligence (AI) is fast taking over various tasks in businesses across sectors. Typically, it is put to use to carry out functions that need accuracy but are repetitive in nature, like data analysis. At the other end of the spectrum, the technology is being honed and taken to a cutting-edge levels – unlocking its potential to perform cognitive functions of the kind the human mind performs such as perception, reasoning, problem solving, and even anticipating forthcoming situations, such as supply chain choking for instance, and be ready with solutions.
Other than the familiar data analysis, AI is currently used commonly in areas like Speech Recognition, Robotic Process Automation, Natural Language Generation, Bio-metrics et al. Likewise, the applications range from converting human speech into a format that a computer can understand and apply, use algorithms to device machine learning platforms (like the ones Amazon, Google, Microsoft etc. use), and Bio-metrics that recognise, measure and analyse human’s physical features and behaviour.

And while the potential of AI is limitless, businesses by and large are just beginning to harness that basic benefits of the technology. Even so, recent studies suggest that software for machine learning and deep learning segments of AI are expected to rake in a staggering $90 billion in revenue by 2025.
Companies are deploying the tech to achieve new levels of efficiency in data analysis to run functions and boost bottom lines. This in turn is driving research in the domain itself at a furious pace. Tech giants are pouring in enormous rerun sources into exploring the depth and scope of next-gen AI solutions. In fact, data scientists at internet giants as well as start-ups are teaming up with business managers to get machines to do number crunching that enables decision-making a low-risk affair so as to optimise revenue in businesses. Indeed, the next decade is likely to see seminal changes in both – extensive use of AI to run organisations as also the unleashing of the staggering breadth and scope of AI itself.
Top 15 AI technologies in currency today:
— Natural Language Generation
— Speech Recognition
— Machine Learning Platforms
— Virtual Agents
— Decision Managements
— Ai Optimized Hardware
— Deep Learning Platforms
— Robotic Process Automation
— Text Analytics and Natural Alnguage Processing (NLP)
— Bio-metrics
— Cyber Defence
— Content Creation
— Emotion Recognition
— Image Recognition
— Marketing Automation

Selenium

Selenium: Versatile Software Testing automation tool

Selenium is one of the most widely used open-source WebUI (User Interface) Automation Testing suite, originally developed in 2004. It has a portable framework which provides a playback tool for authoring functional tests without the need to learn a test scripting language. The testing tool provides a test domain-specific language to write test cases where the developer can use programming languages, including C#, Java, Perl, PHP, Python, Ruby, Scala, Groovy.

It’s a suite, not just one tool

Intrinsically, Selenium it is not a single tool, instead it is a product suite consisting of:

-- Selenium WebDriver

-- Selenium RC (Remote Control)

-- Selenium IDE

--Selenium-Grid

The Selenium IDE is the simplest framework in the suite and is the easiest one to learn. It is a Firefox plugin that testers can install on their PCs. However, because of its simplicity, Selenium IDE should only be used as a prototyping tool. For creating more advanced test cases, testers might have to use either Selenium RC or WebDriver.

Selenium can be used to automate functional tests and can be integrated with automation test tools such as Maven, Jenkins and Docker to achieve continuous testing. It can also be integrated with tools such as TestNG and JUnit for managing test cases and generating reports. Selenium-Grid is a feature that allows you to run test cases in different machines across different platforms. The control of triggering the test cases is on the local machine, and when the test cases are triggered, they are automatically executed by the remote machine.

Salient features

-- Ensures transparency, agility and transparency across the cross-functional teams of SDLC process (developers, quality assurance, operations, clients and the management).

-- Avoids waste of tester’s time in writing test scripts for each platform to be tested as it follows the principle of writing one test script and runs on any platform.

-- Fosters delivery integration efforts by automating the test process.

-- Offers great visibility in cases of end-to-end applications testing.

-- Reduces turnaround time by facilitating testing teams to automatically run multiple test cases parallelly on multi-browser platforms. This reduces the turnaround time by ensuring extreme testing quality.

-- Allows integration with other tools and jars like ExtentReports, Sikuli and Appium that extend its own functionalities too.

Why testers prefer it

Testers prefer Selenium over any other tool due to its ease of use, availability and simplicity. With the introduction of Selenium RC, testers can now circumvent the restrictions imposed by Same Origin policy prohibits JavaScript code from accessing elements from a domain that is different from where it was launched. Besides that, Selenium also encourages testers to write a script in one programming language and run (re-use) the same test scripts on multiple browser platforms.

Selenium’s WebDriver is of late becoming standard for all browsers, which in turn will automatically support it. The interesting aspect of WebDriver is it leverages testers in testing UI modules, offers a large set of options to test, compare results and finally check if they are in accordance to the expected application behaviour.

Nutanix @ Attuneww

Nutanix: Leader in Hyperconvergence space

The concept

Nutanix is an Enterprise Cloud OS based on hyperconvergened technology which serves as a building block for private clouds. Its solutions are based on the industry’s most popular hyperconverged infrastructure (HCI) technology i.e. a completely software-defined stack that integrates compute, virtualisation, storage, networking and security to run any application at any scale. Nutanix essentially offers a solution to data center complexities, scalability and flexibility issues with its hyperconverged technology and equipment.

The benefits

With Nutanix, client businesses obtain a software or operating system that has the ability to reap the functionality of multiple clouds all at the same time, and consequently produce a management system that is scalable, simple and yet workload-oriented. From the users’ point of view, this technology is tuned to scale up a business easily on a shared-nothing architecture with no single point of failure, and no data and meta-data redundancy. It is built to deliver a software-defined enterprise cloud that can run applications at any scale.

Low cost, high yield:

-- Lower costs: 40-60% reduction in overall CapEx and OpEx.

-- Limitless Scalability: Scale infrastructure predictably and linearly, without limits.

-- Fastest time-to-value: Eight-times faster time-to-value in buying, deploying and managing.

-- Smallest footprint: Up to 90% reduction in power, cooling and space, with a 2U form factor.

-- Spares time for IT innovation: Frees up IT resources to focus on important initiatives and innovation.

How it works

All solutions featured by Nutanix are software defined and hyperconverged, meaning they operate on a virtualized and integrated infrastructure of computing, storage, and networking that are available as a full stack. It delivers a web-scale, hyperconverged infrastructure solution purpose-built for virtualization and cloud environments. This solution brings the scale, resilience, and economic benefits of web-scale architecture to companies through the Nutanix Enterprise Cloud Platform, which combines three product families -- Nutanix Acropolis, Nutanix Prism and Nutanix Calm.

The attributes

As an Enterprise Cloud OS, Nutanix is optimized for storage and compute resources. It employs machine learning to plan for and adapt to changing conditions automatically, and is self-healing to tolerate and adjust to component failures.

Features at a glance:

 -- API-based automation and rich analytics.

 -- Simplified one-click upgrade.

-- Native file services for user and application data.

-- Native backup and disaster recovery solutions.

-- Powerful and feature-rich virtualization.

-- Flexible software-defined networking for visualization, automation, and security.

-- Cloud automation and life cycle management.

Azure: Microsoft’s popular Cloud Service Platform

It is no secret that cloud computing, with its vast applications for business continuity, collaboration etc, is rapidly becoming an agent of change in how businesses operate worldwide. Its game-changing nature of securely hosting and saving files online has brought about vastly increased competitiveness in businesses through cost reduction, greater flexibility and optimal resource utilization. Cloud technology has gained immense popularity among mid-size and small business, thanks to its ability to allow access to application software over high-speed internet connection without the need for investing in computer software and hardware. The leading tech giant in the cloud computing industry today are Amazon, Google, Microsoft, IBM and Oracle, among others. Of these, Microsoft has been at the forefront of cloud technology world for quite a few years now.

Cloud service categories

Azure cloud services fall into three main categories: IaaS (Infrastructure as a Service), PaaS (Platform as a Service) and SaaS (Software as a Service). All three options provide secure, reliable access to cloud hosted data built on Microsoft’s stable architecture. Here, IaaS is the most all-encompassing offering which gives you a server in the cloud. It allows the user to be in full control over the virtual machine, while the company is responsible for managing everything from the operating system up to the application that are running. PaaS and SaaS are the other services where applications are built and hosted through third-party vendors that usually charge for a certain amount of service.

Azure gained the edge

The rising popularity of Microsoft Azure is attributed of the comprehensive set of features and capabilities it offers. The company invested lavishly in its Azure infrastructure which provides hybrid computing tasks -- (i.e. running jobs on both the Azure Public Cloud and on the data center of the customer). Hybrid computing is useful to save on investment costs because the cloud is scalable, and a business can use Infrastructure as a Service only for specific tasks while still keeping its data center for more applicable (or less intricate) computing jobs.

Services Azure provides

Microsoft has been expanding its Azure services with time. Among these are Virtual Machines, SQL databases, Application services etc. The products it provides include AI + Machine Learning, Analytics, Blockchain, Compute, Containers, Databases, Developer Tools, DevOps, Identity, Integration, Internet of Things, Management and Governance, Media, Migration, Mobile, among others.

How businesses benefit

Azure comes in handy to companies that need additional computer capacity for existing applications, but don’t want to add more servers to their own data centers. It allows easy migration of workloads to the cloud without having to modify network configurations while still being able to connect the virtual machines to your on-premises corporate networks. Microsoft Azure has a worldwide network of data centers for its cloud platform, spread over 55 regions in 140 different countries. These form the backbone of facilities like building, deploying, and managing services and applications, anywhere.

-- Azure helps businesses be future-ready -- thanks to continuous innovation from Microsoft.

-- Helps companies build infrastructure on their terms, with a commitment to open source and support for all languages and frameworks, build how you want and deploy where you want to.

-- Enables operations of hybrid cloud seamlessly by integrating and managing environments with services designed for hybrid cloud.

-- It is ensures high security from the ground up, backed by a team of experts and proactive compliance trusted by enterprises, governments and startups.

Azure is the future

Intelligent Cloud and Intelligent Edge with Artificial Intelligence running across all systems is the next big thing. Azure platform is ready to provide an agile and secure experience in these.

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ModelListener Hook in Liferay DXP 7.1

Model Listeners are used for listening to the Liferay Model Events.Model Listeners implement the ModelListener interface. They are used to listen for persistence events on models and perform actions based on requirement either before or after creation of event.

Model listeners were designed to perform lightweight actions in response to a create, remove, or update attempt on an entity’s database table or a mapping table.

For example, if one wants to perform a specific action after deleting a user, this can be implemented using a model listener.

Requirements:Liferay 7 Dxp and knowledge of creating liferay hooks

You can create a model listener in a module by doing two simple things:

●     Implement ModelListener

●     Register the service in Liferay’s OSGi runtime

Step 1: Create a model Listener class that extends BaseModelListener to override listener methods from parent class. Here in CustomEntity you can define any entity of liferay as well as custom entity also. Here I have used User as an entity.

Step 2: Register the Model Listener Service

Register the service with Liferay’s OSGi runtime. If using Declarative Services, set service= ModelListener.class and immediate=true in the Component.

Step 3: Now implement listener methods and write your custom logic to perform actions. Here I have used onAfterRemove() and onBeforeRemove().

There are multiple methods of model listener used to perform action on model.

Listening for Persistence Events:

onAfterAddAssociation	onBeforeAddAssociation
onAfterCreate	onBeforeCreate
onAfterUpdate	onBeforeUpdate
onAfterRemove	onBeforeRemove
onAfterRemoveAssociation	onBeforeRemoveAssociation

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ADVANTAGES OF LIFERAY 7/DXP DEVELOPMENT TRAINING

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Learn Use of Hook in Liferay with examples

 https://www.youtube.com/watch?v=oX9qsUjPsGU

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