What is Visual Studio Code?

Visual Studio Code (VS Code) is a free, open-source code editor developed by Microsoft. It is widely used for programming, supporting multiple programming languages with features such as debugging, syntax highlighting, intelligent code completion, snippets, and code refactoring. It is lightweight, yet powerful, and runs on Windows, macOS, and Linux.

Top Use Cases of Visual Studio Code

1. Web Development: Supports HTML, CSS, JavaScript, and modern frameworks and libraries like React, Angular, and Vue.js.
2. Cloud-Native Development: Integrated with Azure and supports Docker and Kubernetes for developing and deploying microservices.
3. Data Science: Supports Python with extensions for Jupyter Notebooks, data visualization, and machine learning.
4. Application Development: Supports languages like C#, Java, Python, and others, useful for both desktop and mobile application development.
5. Extension Development: Developers can create their own extensions to add new languages, themes, debuggers, and to connect to additional services.

Features of Visual Studio Code

• IntelliSense: Provides smart completions based on variable types, function definitions, and imported modules.
• Debugging: Built-in debugging support for Node.js, JavaScript, and TypeScript, with extensions for other languages like Python and PHP.
• Git Integration: Offers built-in Git commands for committing, pulling, and pushing changes to a repository.
• Extensions: A rich ecosystem of extensions to enhance functionality for different languages and tools.
• Customizable: Highly customizable through JSON settings, allowing users to tweak the editor’s appearance and behavior.

Workflow of Visual Studio Code

1. Setup: Install VS Code and relevant extensions for your development environment.
2. Project Initialization: Open or create a new project and configure workspace settings.
3. Coding: Write code with IntelliSense assistance for more efficient coding.
4. Version Control: Use integrated Git support for version control.
5. Debugging: Use the built-in debugger to set breakpoints, inspect variables, and step through code.
6. Testing and Deployment: Utilize extensions for deploying applications or running tests.

How Visual Studio Code Works & Architecture

Visual Studio Code operates on an architecture that separates the frontend (the editor itself, which runs as a web application) from the backend services. The backend is powered by Node.js, which handles extensions and integrations. This separation ensures that VS Code remains responsive, regardless of what operations are being executed in the background.

How to Install and Configure Visual Studio Code

1. Download and Install: Go to the Visual Studio Code website, download the appropriate version for your OS, and install it.
2. Open and Setup: Open VS Code and install essential extensions (e.g., for Python, install the Python extension).
3. Configure Settings: Customize settings by accessing File -> Preferences -> Settings. Configure user and workspace settings as needed.

Step-by-Step Tutorials for Visual Studio Code: “Hello World” Program

Example with Python:

Install Python Extension: From the extensions tab, search for ‘Python’ and install it.

1. Create a New File:
   • Open VS Code, then go to File > New File and save it with an appropriate name, like hello_world.py if you are using Python.
2. Write the Code:
   • For a Python file, enter: print("Hello, World!")
3. Run the Program:
   • Open the integrated terminal (Terminal > New Terminal) and type python hello_world.py to execute the code.
4. View Output:
   • The message “Hello, World!” will be displayed in the terminal, indicating that your program has run successfully.

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What is Framework?

A framework is a structure or set of rules that provides a foundation for something. In software engineering, a framework is a set of pre-written code that provides the basic structure for an application. Frameworks can be used to speed up the development process and make it easier to create complex applications.

Frameworks are typically associated with a specific programming language. For example, Ruby on Rails is a framework for the Ruby programming language. Django and Flask are two different frameworks for the Python programming language.

Here are some examples of frameworks:

• Laravel: Laravel is a framework for the PHP programming language. It is known for its elegance and expressiveness.

• Spring Boot: Spring Boot is a framework for the Java programming language. It is known for its ease of use and production-ready features.

• Flask: Flask is a microframework for the Python programming language. It is known for its simplicity and flexibility.

• Django: Django is a framework for the Python programming language. It is known for its scalability and security.

• Ruby on Rails: Ruby on Rails is a framework for the Ruby programming language. It is known for its rapid development and easy-to-use syntax.

Why Frameworks used?

Frameworks can be used for a variety of tasks, such as:

• Web development: Frameworks like Django, Ruby on Rails, and Laravel provide pre-built features and functionality to speed up the development of web applications.

• Mobile app development: Frameworks like React Native, Flutter, and Xamarin offer a way to build mobile applications across multiple platforms using a single codebase.

• GUI development: Frameworks such as Qt, Tkinter, and Electron provide tools for creating graphical user interfaces for desktop applications.

• Data analysis and machine learning: Frameworks like TensorFlow, PyTorch, and Scikit-learn offer tools and libraries to build and deploy machine learning models and perform data analysis tasks.

• Testing: Frameworks like Selenium, JUnit, and pytest help automate the testing process for software applications, making it easier to write and execute tests.

• API development: Frameworks like Flask and Express.js provide tools for creating and managing APIs, allowing developers to build robust and scalable web services.

• Game development: Frameworks such as Unity and Unreal Engine help developers create and build interactive and immersive gaming experiences.

• Content management systems: Frameworks like WordPress, Drupal, and Joomla provide a foundation for creating customizable and feature-rich websites and blogs.

• Real-time applications: Frameworks like Socket.io and SignalR enable developers to build real-time applications, such as chat applications, collaborative tools, and multiplayer games.

• Internet of Things (IoT): Frameworks like Arduino, Raspberry Pi, and Node-RED offer tools and libraries to develop IoT applications, allowing devices to interact and communicate with each other.

Purpose of Framework

The purpose of a framework is to provide developers with a structured and comprehensive set of tools, libraries, and methodologies to develop software applications more efficiently. It provides a foundation and predefined structure for building software, allowing developers to focus on the specific features and functionality of their application rather than writing code from scratch.

Some key purposes of using a framework include:

1. Efficiency: Frameworks provide pre-built components and libraries that speed up development time by eliminating the need to write repetitive code. Developers can leverage these pre-existing functions and features to quickly build applications.
2. Consistency: Frameworks often have common standards and conventions that help maintain consistency in coding style and structure across a project or development team. This makes the codebase more maintainable and easier to understand for all team members.
3. Scalability: Frameworks are designed to be modular and extensible, making it easier to add new features and functionalities to an application as it grows. This scalability allows developers to easily accommodate future changes and enhancements.
4. Security: Many frameworks incorporate security best practices and standards to prevent common software vulnerabilities. By following the framework’s guidelines and using its built-in security measures, developers can ensure that their applications are more secure.
5. Community Support: Frameworks often have active and large developer communities that provide support, guidance, and a wealth of resources. This community support can be valuable when developers encounter challenges or need assistance in implementing specific features.
6. Code Reusability: Frameworks encourage code reusability by providing predefined libraries, modules, and components. This allows developers to reuse already implemented functionality, minimizing the need for rewriting code and reducing the potential for errors.

Types of Framework

There are various types of frameworks available, each designed for specific purposes and technologies. Some common types include:

1. Web Frameworks: These frameworks are used for developing web applications and websites. They provide tools and libraries for handling tasks such as routing, session management, and database integration.
2. Mobile Frameworks: Mobile frameworks are used for building mobile applications for platforms such as iOS and Android. They often include features like UI components, device compatibility, and data synchronization.
3. Testing Frameworks: Testing frameworks are used for automating the testing process of software applications. They provide tools for writing test cases, executing tests, and generating reports.
4. UI Frameworks: UI frameworks are focused on user interface development. They provide pre-designed components and templates that developers can use to create visually appealing and responsive interfaces.

Benefits of Framework

Using a framework in software development offers several benefits, including:

1. Productivity: Frameworks provide a foundation of reusable code and libraries, which reduces the amount of time and effort required to build an application from scratch. This allows developers to focus on the specific requirements of their project and accelerate the development process.
2. Consistency: Frameworks enforce a set of rules and best practices, which ensures consistency in the codebase. This makes it easier for developers to understand and maintain the application, even when working in teams or transitioning between projects.
3. Scalability: Frameworks are designed to handle scalability challenges. They offer features like modular architecture, caching mechanisms, and database optimization, which enable applications to handle increasing amounts of data and traffic.
4. Security: Frameworks often include built-in security features and mechanisms, such as encryption, authentication, and input validation. This helps developers protect their applications from common security vulnerabilities and ensures data integrity.

In conclusion, a framework is a powerful tool in software development that provides a structured approach to building applications. It simplifies the development process, improves productivity, ensures consistency, and offers scalability and security benefits. By understanding the concept of framework and exploring its different types and benefits, developers can leverage this tool to create efficient and robust applications.

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What is DevOps?

DevOps is a software development methodology that combines software development (Dev) and IT operations (Ops) to improve collaboration, efficiency, and quality in the software development lifecycle.

Why We need DevOps?

We need DevOps because it allows organizations to deliver software faster and more reliably. By combining development and operations teams, organizations can automate processes, streamline workflows, and increase the speed of software delivery.

What is the Advantage of DevOps?

The advantages of DevOps include:

1. Increased collaboration and communication: DevOps promotes collaboration and communication between development teams, operations teams, and other stakeholders. This results in better alignment, shared goals, and faster resolution of issues.
2. Continuous delivery and faster time to market: DevOps enables teams to automate various stages of the software delivery process, such as building, testing, and deploying. This automation leads to faster, more frequent and reliable releases, ultimately reducing time to market.
3. Improved quality and stability: Continuous integration, automated testing, and deployment pipelines ensure that software changes are thoroughly tested and validated. This leads to higher quality releases with fewer bugs and reduces the risk of system failures.
4. Increased scalability and efficiency: DevOps practices emphasize scalability and efficiency by leveraging automation, infrastructure-as-code, and containerization. This allows for easier provisioning and scaling of resources to meet changing demands, ultimately optimizing resource utilization.
5. Enhanced customer satisfaction: DevOps focuses on delivering value to customers by rapidly responding to their needs and incorporating their feedback into new releases. This customer-centric approach improves satisfaction and loyalty.
6. Better risk management: DevOps practices encourage monitoring, logging, and continuous feedback loops, which enable teams to identify and resolve issues quickly. This proactive approach minimizes the impact of failures and reduces downtime.
7. Empowered and more motivated teams: DevOps eliminates silos and encourages cross-functional collaboration, empowering team members to contribute to different stages of the software lifecycle. This autonomy and shared responsibility result in higher engagement, motivation, and job satisfaction.

What is the feature of DevOps?

The key features of DevOps include:

• Continuous Integration (CI): DevOps emphasizes the practice of integrating code changes frequently and automatically, allowing teams to detect issues early.
• Continuous Delivery (CD): DevOps promotes the idea of delivering software updates frequently and reliably, ensuring that new features and bug fixes reach users quickly.
• Infrastructure as Code (IaC): DevOps encourages the use of code to manage and provision infrastructure, making it easier to replicate environments and reduce manual efforts.
• Automated Testing: DevOps advocates for the automation of testing processes, enabling teams to identify and fix issues quickly and efficiently.

Top 10 Use Cases of DevOps

1. Continuous Deployment: DevOps enables organizations to continuously deploy code changes to production, reducing the time between development and deployment.
2. Infrastructure Automation: DevOps allows for the automation of infrastructure provisioning and management, making it easier to scale and manage resources.
3. Continuous Monitoring: DevOps promotes continuous monitoring of applications and infrastructure, allowing teams to identify and address issues in real-time.
4. Microservices Architecture: DevOps is well-suited for organizations adopting a microservices architecture, as it enables teams to deploy and manage individual services independently.
5. Agile Development: DevOps aligns well with agile development methodologies, as it encourages collaboration, frequent feedback, and continuous improvement.
6. Release Management: DevOps helps organizations streamline the release management process, allowing for faster and more reliable software releases.
7. Cloud Migration: DevOps can facilitate the migration of applications and infrastructure to the cloud, enabling organizations to take advantage of cloud services and scalability.
8. Security and Compliance: DevOps incorporates security and compliance practices into the development process, ensuring that applications meet the necessary standards.
9. Containerization: DevOps supports the use of containerization technologies like Docker, making it easier to deploy and manage applications across different environments.
10. DevSecOps: DevOps can be extended to include security practices, resulting in DevSecOps, which integrates security into every stage of the software development lifecycle.

How to Implement DevOps?

To implement DevOps, organizations can follow these steps:

1. Assess the current state: Understand the existing development and operations processes, identify pain points, and define objectives for the DevOps implementation.
2. Build a DevOps team: Assemble a cross-functional team with members from development, operations, and other relevant departments to drive the implementation.
3. Define processes and tools: Establish the processes and tools needed for collaboration, automation, and monitoring. This may include version control systems, continuous integration and delivery tools, and infrastructure management platforms.
4. Automate processes: Identify opportunities for automation and implement tools and technologies that can automate tasks such as code deployment, testing, and infrastructure provisioning.
5. Foster collaboration: Encourage communication and collaboration between development and operations teams to break down silos and promote shared responsibility.
6. Measure and improve: Define metrics and key performance indicators (KPIs) to track the success of the DevOps implementation. Continuously monitor and analyze data to identify areas for improvement.

How to Get Certified in DevOps?

There are a number of certifications available for DevOps professionals. Some of the most popular certifications include:

• DevOps Engineer – Certified Professional (DevOps CERT): This certification is offered by the DevOps Institute and is designed for professionals with experience in DevOps. The exam covers a wide range of topics, including continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), and cloud computing.

• Certified DevOps Engineer – Associate (CDevOps-A): This certification is offered by the Linux Foundation and is designed for professionals with a basic understanding of DevOps. The exam covers topics such as automation, communication, and collaboration.

• Certified DevOps Engineer – Expert (CDevOps-E): This certification is also offered by the Linux Foundation and is designed for professionals with experience in DevOps. The exam covers a more advanced level of topics, such as security and compliance.

• Certified Kubernetes Administrator (CKA): This certification is offered by the Cloud Native Computing Foundation and is designed for professionals who want to manage Kubernetes clusters. The exam covers topics such as cluster administration, networking, and security.

• Certified Kubernetes Application Developer (CKAD): This certification is also offered by the Cloud Native Computing Foundation and is designed for professionals who want to develop applications for Kubernetes. The exam covers topics such as application development, deployment, and monitoring.

The best certification for you will depend on your experience level and career goals. If you are new to DevOps, then the CDevOps-A certification is a good place to start. If you have more experience, then the CDevOps-E certification or the CKA certification may be a better fit.

To become certified, you will need to take and pass an exam. The exams are typically offered online and are proctored. The cost of the exams varies depending on the certification.

To get certified in DevOps, Visit most popular website for providing DevOps certification Courses .

– DevOpsSchool.com
– scmGalaxy.com
– BestDevOps.com

How to Learn DevOps?

There are a number of ways to learn DevOps. Some of the most popular ways include:

• Taking a course: There are a number of online and in-person courses available for DevOps professionals. Example- DevOpsSchool.com , scmGalaxy.com , BestDevOps.com
• Reading books: There are a number of books available on DevOps.
• Attending conferences: There are a number of conferences held each year that focus on DevOps.
• Joining a community: There are a number of online communities for DevOps professionals.
• Getting hands-on experience: The best way to learn DevOps is by getting hands-on experience. This can be done by volunteering for a DevOps project or by working for a company that uses DevOps practices.

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Software development Life cycle (SDLC) is a process of producing high-quality software at the lowest cost and in possibly less time. Generally, SDLC has well-tested and ready-to-use phases which provide an organization to help in creating high-quality software. ISO/IEC 12207 is an international standard of software life cycle process. This standard defines all the tasks which need to develop and maintain software. SDLC targets to produce high-quality software by meeting the expectations of clients within the time limit and in budget. It is made up of a plan which describes how to develop, maintain, alter, and improve the software.

Why we need SDLC

Basically, SDLC is a method with the process, which helps in creating high-quality software. By this you can understand the whole criteria of producing effective software, that’s why SDLC is important. Without SDLC you can’t create a standard software because it gives a standard way to produce an effective and efficient software that will run in the market with client expectations which will help him in managing his part of work. With time we update the software as per customer feedbacks to get a better result which is also a part of SDLC.

Benefits of the Software Development Lifecycle

• Forms the base for project planning.
• Helps to estimate cost and time.
• It gives the clarity of the project and the development process.
• Enhance the speed and accuracy of development progress.
• Minimizes the risks and maintenance during the project.
• Its given standard improves client relations.
• SDLC implement checks to ensure that the software is well tested before being installed in greater source code
• Developers can’t move to the next step until the prior one is completed by SDLC.

What are the SDLC phases

Software Development Life Cycle Models

Waterfall Model:-

A little too old and harsh model is the Waterfall model. It is one of the old-fashioned SDLC models that is not much preferred in the modern software development ecosystem.

The reason it is not favored much is that it runs on a very inflexible structure conditioning that the entire set of requirements should be laid down from the very beginning of a project. This limits the freedom and flexibility of the actual design and development of software.

After completing the development, the product goes through the test for meeting its initial requirements. If it is not good enough, it is to be restructured, which is a lot of work.

Usually, software development companies resist dealing with Waterfall though it still seems to be an effective model for the handful of projects.

RAD Model:-

The rapid Application Development (RAD) process is an adoption of the waterfall model. It aims to developing software in a short period. The RAD model is based on prototyping and iterative development with no specific planning involved. The process of writing the software itself involves the required planning for developing the product. The RAD model is based on the concept that a better system can be developed in less time by using focus groups to collect system requirements

• Business Modeling
• Data Modeling
• Process Modeling
• Application Generation
• Testing and Turnover

 Spiral Model:-

The spiral model is a risk-based process model. This SDLC model helps the group to adopt elements of one or more process models like waterfall, incremental, etc. The spiral technique is a combination of fast prototyping and concurrency in design and development activities. The following will explain the typical uses of a Spiral Model –

• When there is a budget compellable and risk evaluation is important.
• For intermediate to high-risk projects.
• Long-term project commitment because of probable changes to economic priorities as the requirements change with time.
• Customer is not sure of their requirements which is ordinarily the case.
• Requirements are complicated and need evaluation to get clarity.
• Some changes are expected in the product during the development cycle.

V-Model :-

In this model execution of processes happens in a sequential method in a ‘V-shape’. It is also known as ‘Verification and Validation model’. The V-Model is an Expansion of the waterfall model and is based on the association of a testing phase for each related development stage. That means for every single phase in the development cycle, there is a directly associated testing stage. This is a disciplined model and the next phase starts only after completion of the previous phase.

Incremental Model :-

The incremental model is not a distinct model. It is radically a series of waterfall cycles. The requirements are divided into groups at the initial stage of the project. For each group, the SDLC model is adhered to develop software. The SDLC process repeats with each release adding more functionality till all requirements are met.

use of  Incremental Model:-

• When the requirements are much superior.
• A project has a lengthy development program.
• When Software team are not well skilful or trained.
• When the customer demands an immediate release of the product.
• You can develop precedence requirements first.

Agile Model :-  

The agile model is a model which promotes continuous interaction of development and testing during the SDLC process of any project. The agile model is a combination of iterative and incremental process models with aims on process and customer satisfaction by continuous delivery of working software products. Agile Methods have divided the product into small incremental builds. These builds are issued in iterations. Each iteration lasts from typically one to three weeks. Every iteration involves cross-functional teams working together on various areas like

• Planning
• Requirements Analysis
• Design
• Coding
• Unit Testing and
• Acceptance Testing.

At the end of the iteration, a functional product is displayed to the customer.

Iterative Model :-

In the iterative model, the iterative process starts with the implementation of a small set of software requirements, makes  enhancements in the evolving versions till the complete system is implemented and ready to deploy on the market. In this model development of the life cycle doesn’t start with full requirements, instead, it begins with the implementation of just a part of the software, which will be reviewed to identify further requirements later. This process is repeated till the new version of the software is produced at the end.

Big bang model :- 

The big bang model comprises focusing all types of possible resources in software development and coding with little bit or no planning. This model works best for small projects with the smaller size development team who works together. It is useful in academic software projects as well. It is also an ideal model where requirements are either unknown or a final release date is not provided.

Advantages of the Big Bang Model

• This is very easy to use model
• Little bit or no planning required
• Easy to handle
• Very few resources are needed
• provides flexibility to developers

Disadvantages of the Big Bang Model

• Very High risk & uncertainty.
• Not a good model for difficult and object-oriented projects.
• Poor model for long-term and ongoing projects.
• Can become very expensive if requirements are not properly understood.

Prototype Model :-

The prototype model starts with the gathering of required information to start the development process. In this the developer meets the client, understand the purpose of software and identify the actual requirement. Then a quick design is created, focused on each aspect of the software which will be visible to the user. Then it goes ahead with the development of prototype, customer checks and try to identify if any modification needs to be done. In this step, looping occurs and better versions of prototype are created. It continuously happens being in touch with client to show him if any further requirements needs to be done.  This process remains continue till the user is satisfied. Once the user is satisfied, the prototype is converted into the actual system to deploy in market.

DevOps:-  Let’s understand. In the agile Model, both Development and testing activities were concurrent, unlike the waterfall model. It was lost on practices that didn’t come up to speed with agile practices. Due to lack of collaborations between developers ad the operations team, slow down the development process and releases. Then software companies started realizing the need for better collaboration between teams and faster delivery of software. It gave birth to the DevOps approach. DevOps enabled fast software delivery with minimum problems to fix and faster resolution of problems. The term DevOps is deprived of two words development and operations. DevOps is a practice that allows a single team to manage the whole application development life cycle, i.e. development, testing, deployment, etc. The aim of DevOps is to shorten the development life cycle. DevOps is a software development approach that helps in producing high-quality software with reliability and in less time. DevOps is a software development method that aims at communication, integration, and collaboration between IT professionals to enable continuous deployment of products.

Which SDLC Model is Best

As far I have understood, DevOps is the best model in today’s software ecosystem which provides a better development life cycle with high effectiveness and efficiency in work progress.  But it doesn’t mean rest models are not useful, they are also useful and still getting used by some organizations who feel that model is best in their work. DevOps is a practice of bringing development and operation teams together whereas, Agile refers to the continuous iterative approach, which aims at collaboration, customer feedback, small, and continuous releases. DevOps’ purpose is to manage end-to-end engineering processes. It helps in increasing an organization’s speed to deliver applications and services.  The agile purpose is to manage difficult projects. The agile development process divides the product into smaller pieces and integrates them for final testing. It can be implemented in many ways, including Scrum, XP, etc.

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Aside from ‘COVID-19’, one of the key buzzwords of 2020 is undoubtedly ‘AI’ – artificial intelligence. A technology once reserved for technologically minded people alone is now seeping into the more everyday, ordinary lives of us all. So much so that many are fearing a not-so-affectionately coined ‘robot takeover’ is on the horizon. 

Yet it’s not all as insidious as those more cynical among us would have you believe. AI has truly proven its worth this year in streamlining business operations in-face of COVID restrictions, and continues to further lend its robotic hand in healthcare. But where is it taking us into 2021?

Automation in a nation divided

As the UK steps out of its second lockdown, Animesh Chowdhury, Founder & CTO at Goodtill, explains how, for pandemic recovery in hospitality, automation is no longer a survival tool; instead, it will be utilised to enhance processes and boost long-term efficiency:  

“As we emerge from lockdown, those who remain in tier three must keep their doors closed, while pubs, restaurants and bars in tiers one and two are once again allowed to re-open. However, by implementing tools such as electronic point-of-sale (EPOS) and online ordering platforms, these hospitality businesses can pivot services to allow click-and-collect services, continuing revenue streams while under tough restrictions. 

“Many sports stadiums allowing fans back in won’t be opening their concourses for food and drink at first, in fear of excessive queueing. However, those leveraging an EPOS system can facilitate orderly click-and-collect stations, where fans purchase refreshments through an app. To the lower league sports teams, this could make the world of difference in income.”

Maintaining human contact

According to the CBI, nine in ten workers will need to learn new skills or be retrained entirely over the next decade. This speaks volumes: 21 million Britons currently lack ‘basic digital skills’. 

Martin Taylor, Deputy CEO & Co-Founder of Content Guru, explains what this means for the contact centre industry:

“One of the effects of the pandemic has been to fast-track automation and artificial intelligence technologies as a matter of necessity in industries everywhere – and nowhere more so than the contact centre sector. New and entirely more fulfilling contact centre roles are opening up for humans thanks to advancements in automation, AI and Natural Language Processing.

“I strongly believe in the concept that AI should assist human contact centre workers, rather than succeed them. We work to develop AI technology that integrates easily into digital customer channels, quickening decision-making and making it easier for contact centre workers to match customers with their most relevant products, services and information.” 

Richard Buxton, Director of N4 Engage, also shares his predictions on where AI and machine learning (ML) fit into the communications and contact centre space:

“These technologies can not only deliver information in a timely, accurate and repeatable way, but they’re also sophisticated enough for communications to seamlessly transition from a bot to a human – all without losing that feeling of human interactions.

“Bots aren’t necessarily replacing humans, just adding efficiency and freeing up agent time for more complex queries. A bot can manage most queries, which means the business will save money. The human agents will be more knowledgeable, and the workforce will be evolving to become more expert – with less people simply reading from scripts.

“COVID-19 has been an accelerant to moving the workforce from a service role to a more development/technology role, so organisations need to build up advanced technology skills. This will create new future career opportunities – there is a need for industry to start to invest in developing these skills to ensure that demand is met.”

All-seeing robots?

Rishi Lodhia, Managing Director EMEA at Eagle Eye Networks, delves into the growing market for AI-powered surveillance analytic technologies and the role it has played throughout the pandemic: 

“Advances in AI are such that cloud video surveillance is now transforming how companies manage their security and business operations.

“Within video surveillance the scope of AI-enabled applications is growing all the time and includes options such as monitoring employee arrivals, the presence of intruders, vehicle detection, moisture detection on floors, and other smart features such as loitering and mask detection. 

“AI-powered surveillance has also been applied to help organisations and their teams keep within current COVID-19 compliance guidelines. Consider how we’ve all had to change our behaviour this year – wearing masks, keeping to smaller groups. Integrating AI with cloud-based video surveillance has enabled these rules to be monitored, in real time.

“These capabilities not only improve analysis, speed and accuracy of the surveillance process, but they allow the technology to be applied to a much wider range of operational, efficiency and safety use cases than traditional CCTV.”

Another type of cyber threat?

As AI, automation and other emerging technologies have evolved in recent years, their capabilities have become a driving force shaping modern cybersecurity solutions.

Samantha Humphries, Security Strategist at Exabeam, comments:

“According to Exabeam’s 2020 Cybersecurity Professionals Salary, Skills and Stress Report, 88% of cybersecurity professionals believe automation will make their jobs easier.

“However, whilst AI and automation are helping to reduce response times and bolstering defence, there is also some concern from security professionals that these technologies will displace employees. 

“With almost half (47%) of workers believing automation is a threat to their jobs as we head into the new year, security leaders should reassure employees these evolving technologies improve productivity and outcomes rather than eliminate jobs.

“AI and automation provide security professionals with greater opportunities to transition from lower-valued activities, such as data inputting, to high-profile, strategic projects. In 2021, security leaders should focus on alleviating concerns around these by sharing best practices and communicating possibilities for growth.”

Automating efficiently

It’s also important to consider that while implementing new technologies will help lower the cost of some processes, automation doesn’t necessarily equal efficacy. Stephen Roostan, VP EMEA at Kenna Security, explains:

“Organisations will need to understand which are the right processes to automate. If the process is wrong to start with, automating it won’t create efficiency in itself, and even if it appears to, it might only be moving the problem onto another team or into a different department.  

“Before deploying new technology, enterprises should take the opportunity to rethink the process and reassess its value to the business. Getting it right from the outset will ensure that automation becomes an effective tool to measurably increase efficiency and optimise costs.

“Another consideration that IT teams need to keep in mind is the value that automation can bring to collaboration. With the remote working revolution being at the forefront of 2020, automation and data science tools have already made home-working a much easier process and they will continue to do so in 2021.”

Jim Darragh, CEO of Totalmobile also argues that the most important aspect of introducing new technologies is knowing where to stop, particularly in the public sector: 

“It’s critical that organisations find the right balance between human and machine and businesses need to focus on providing solutions that help employees do their job more efficiently.

“The public sector has been affected by the pandemic more than most, and the recent government wage freeze announcement puts further strain on employees. However, staff morale isn’t solely dependent on salary,” continues Darragh. “By automating admin tasks, or introducing technology that allows staff to spend more time working from home, public sector businesses can greatly improve the day-to-day work lives of their employees, taking away unnecessary stress factors and keeping them happy in their roles despite the wage freeze.”

A bright future

Agata Nowakowska, Area Vice President at Skillsoft, rounds things off by looking into what particular benefits AI will bring to employee satisfaction and engagement:

“With AI automating many of the more mundane processes, overworked and overstressed teams – such as those working in cybersecurity – are freed up to work on far more rewarding and valuable tasks. This gives employees the opportunity to develop new technical competencies in areas such as analytics and programming, or increased proficiencies in the innately ‘human’ skills such as creativity, critical thinking and negotiation. 

“However, with twice as many women than men likely to work in jobs that will soon be automated, employers have a responsibility to ensure that everyone, regardless of gender, age, or location, shares in the spoils of new technology. This means offering a competitive, agile learning culture that enables reskilling, upskilling and job transitioning opportunities.”

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Over the next five years, we estimate that the global workforce can absorb around 149 million new technology-oriented jobs. Software development accounts for the largest single share of this forecast. It took us only 40 years to progress from 1G to 5G, and, by 2030, the world will be embracing 6G which would bring in more advanced connected solutions to the already interconnected world! Most of the newest technological developments rely on the Internet, and, the Internet of Things is an integral part of technology innovations.

Also, with the covid 19 pandemic making global business shutdown, supply chain disruptions, work from home, social distancing and remoting working a new normal, we are registering a huge surge in the number of connected technologies deployments, especially in the enterprise and automotive sector, where the internet of things market is projected to grow to 5.8 billion endpoints in 2020, a 21% increase from 2019, as predicted by one of the leading analyst firms.

Skill requirements in IoT:

A career in IoT is quite promising for those who have innovative thinking and creative abilities and are looking for an exciting work environment, professional development, and higher compensations that IT professionals. Though, is a perfect domain for scholars interested in contributing to technological advancement, however, it must be warned that it’s not easy to become a ‘deep’ IoT technologist as learning one platform or skillset isn’t enough.

• One need’s have the natural curiosity about the world and the problems where technology can provide a solution.
• The team working on IoT projects comprises of a mix of engineers, website developers, programmers, UI/UX designers, etc.
• Therefore, IoT developers need to develop soft skills, such as, good communication and interpersonal skills for closely working with all of them to help translate connectivity and other data-related goals.
• The ability to learn and apply the concepts by building multidisciplinary skillsets is the key to a successful career in IoT.
• Keeping abreast of all technological changes and trends is a given. New research titled “Top Strategic IoT Trends and technologies through 2023” states the 10 most impactful IoT Trends which include, Artificial Intelligence, Infonomics and data broking.

Shift from intelligent edge to intelligent mesh, IoT governance, Sensor Innovation, Social, legal and ethical IoT, Trusted hardware and OS, latest IoT User experience, Innovation on the chip and new IoT wireless networking technologies.

IoT developers need to develop a inside out knowledge of programming to understand how the data can be collected, sent and stored at the server. Knowing software languages as JavaScript and Python as well as server-side languages such as PHP, ASP.NET, or Node.js. is a must.

• Unlike other developers, those who work in the IoT space must have a deep understanding of sensors and wireless communication. A background in computer science for IoT information analytics, mobile apps, and UI development does help. For IoT device manufacturing, competencies in electrical, electronics and instrumentation engineering would be essential.
• Beyond computing, IoT will take you into the world of mechanical and civil engineering as sensors gather physics data.
• Knowledge of data science and big data management comes as an added advantage. Another important aspect is security. Lack of data security is the greatest threat the Internet of Things applications in all domains and some of the highest paying jobs are of security professionala

Career opportunities in the Internet of Things

HERE IS THE LIST OF IOT CAREER DOMAINS STUDENTS AN CHOOSE FROM

• Data Scientists
• Network professionals
• Embedded Programs Engineer starting from pcb design & manufacture, firmware engineer, thing engineering, complete product lifestyles cycle management, mechanical engineers, sign conditioning, digital sign processing, internet gateway interfacing and so on. Each discipline has a totally wide range of troubles to resolve for IoT.
• Security experts for risk identity, vulnerability evaluation, public key encryption security and wireless community security
• Device and Hardware experts, especially in Sensors and Actuators are required in the R&D facilities of sensor manufacturing groups
• Software Program Engineering with expertise in backend technologies, records scientist, facts analytics, socket programming, fronted and reporting tools, database administrator and many others

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Awarded as the start-up of the year 2019 by Startcom.com, Sydney in the Asia Pacific region, Tachyon Systems Pty Ltd is a technology research entity based in Melbourne.

The Company is developing an Artificial Intelligence (AI) platform, Tachyon or Tachy to strengthen human intelligence in every stage of a Software development life cycle, thus creating enterprise-grade applications in few hours rather than spending months, weeks or years. 

Mr Shanmuga, Founder and Chief Executive Officer of Tachyon Systems Pty Ltd, is the brain behind Tachyon, which he created after identifying the need for such platform in his everyday tasks as an architect. He is an experienced and highly skilled product architect, holding an extensive experience of more than 16 years in software design and development.

He has an experience across several domains, including Enterprise Content Management, Healthcare, In-Car Navigation, Shared Economy, Energy, Financial & Education and Retail. Graduated from Government College of Technology (Coimbatore, India) in Electronics Engineering, he holds a US patent.

He has previously worked with Dell-EMC, Telstra, Siemens-Healthcare, Capgemini and Siemens-Energy.

Tachy is a hyper-intelligent kind of AI technology, which can understand your natural language or even hand-drawn diagrams, be it sequence diagrams, activity diagrams or high-level block diagrams. With the help of a knowledge base of existing applications and through a series of complex algorithms.

Tachy can analyse your business requirements, its pain points and end-user needs. Subsequently, Tachy can automatically generate the functional requirements of the business and map  these system requirements. From there, Tachy can develop a scalable, reliable and secure enterprise grade application from front-end to the backend in your preferred choice of technology, which can be deployed on any cloud or on-premises in real time.

Besides, Tachy also analyse and monitor your applications to ensure its disaster-ready and performing at industry best, saving your money, time and resources.

Tachyon Platform leverages computer vision, natural language processing and other AI capabilities into the process of software development to save valuable time, increase productivity and to assist in maintaining focus on potential problems as opposed to reinventing the wheel.

The applications developed under this platform have enterprise-grade quality, that guarantees extensibility, observability, reliability, scalability, availability, high security and testability, with region specific compliance and industry best practices applied.

The developed applications are a runtime platform agnostic, a deployment platform agnostic and a programming language agnostic.

Industry leaders like Google, Amazon and IBM have recognied Tachyon’s business potential and its concept. Each of them has also funded the value of at least USD 100k credits per year to Tachyon, to a massive total of USD 320k cloud credits at the ideation stage only.

As per Tachyon Systems, this technology funding was obtained via their individual start-up accelerator programs and is the basis for its technical build. Currently self-funded, Tachyon is seeking for funding to lessen its time-to-market.

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There are a growing number of data collaboration solution providers competing to solve the IT management challenges of enterprise data fragmentation. This is the problem where siloed teams all want to work on large data sets, including data from other teams, and so copies of the data are made and shared, resulting in increased costs of storing and managing multiple copies of the same data in an enterprise.

Enter data collaboration, a relatively new approach that is shaking up big data management and is at the cutting edge of data-driven decision making. Data collaboration allows multiple parties to share access to their data, which gives all parties a more holistic view of the project and enables deeper insights. Not only do teams reap the benefits of having immediate access to a wider array of data, IT departments benefit from reduced data fragmentation and lower management costs while simplifying the deployment architecture.

These platforms also offer significant value-added features, such as rapid integration and development, as well as advanced data visualization. At the same time, they eliminate the need to distribute and control additional copies of data. Data collaboration does away with the necessity of developing APIs in applications or planning complex solutions to make the data fully accessible — all of which can take months before teams can even get their projects started. Instead, users can directly leverage the platform tools to build solutions rapidly and turn around a digital service without the need for skilled software developers to write extensive code. Think of it as what CMS products did for web publishing, or what cell phone cameras have done for content creation. It produces instant results by removing all the heavy lifting.

While the benefits are apparent for enterprises, what about organizations or researchers that want to collaborate with third parties or partners outside of their controlled networks? With data collaboration, the biggest boon to organizations is the added security features for controlling access to the data. Since access is provided through the platform itself, the granularity of access can go down to the data cell level. This allows owners to decide which data are shared to maximize collaboration, while ensuring conformity to regulatory and security requirements. There is no need to worry about what a third party will do with personal data: the owner is always in control and no copies are ever distributed.

In business, data collaboration is the key for achieving the next level of strategic decision making. Imagine you want to expand your department store offerings to increase revenue. You already have your own data on what customers are buying in your store, but what are they buying that you don’t already sell? With data collaboration, you could partner with a payment system provider and find out what customers spend their money on. Critically, not only would you know what your customers are buying from your store versus elsewhere, but you could also see what purchases they are making on the same day. That additional data from a collaborator could tell you if you should open a grocery section and whether it can effectively compete by saving your customers an extra trip. The advantages that data collaboration offer to businesses is obvious.

Magical unicorn?

By now, data collaboration may start to sound like a magical unicorn. How is this practical or even possible? How can you achieve insight when you don’t possess the data? The reality is that with large data sets, you don’t gain insight by looking at the actual data. There’s just too much of it. Instead, you formulate queries around the questions that need to be asked. It’s the query results that will provide the insightful answers. This is already a common practice with data that you do own, so imagine the possibilities of combining data across markets and industries without the cost and expertise barriers of implementing traditional data sharing, integration and security controls.

All this added insight can be achieved without having to know who the individuals are and what each person is purchasing. As a collaborator, you can ensure that personally identifiable information is not exposed, and your customers’ privacy is maintained, because it is the queries that produce insights. These queries can be shared with other collaborators for mutual benefit. As an IT leader, you can reduce your complexity and costs for information systems and data storage. As a program manager, you can accelerate outcomes by rapidly prototyping and productizing solutions. It’s a revolution for big data with the potential to transform how we make use and make sense of data without compromising on security and privacy.

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As enterprises look to become more agile and move towards a DevOps and continuous testing, the need for microservices has grown manifolds.

Businesses require a next-generation web application firewall (WAF) that enables secure delivery of applications. Software development life cycle (SDLC), is as flexible as the dynamic environment and threat landscape and adapts to the needs of the business. Before considering any solution, make sure it meets the requirements of both development and operations (DevOps) and security teams.

SQL injections, cross-site scripting, access violations, remote file inclusion — running applications in a service mesh architecture don’t eliminate the risk from data leakage or service disruptions. Emerging continuous integration and continuous delivery (CI/CD) technologies disrupt common practices and processes and create new blind spots.

Here are 10 characteristics to look for when considering protection to data and applications in a service mesh architecture.

Native Fit into CI/CD Pipeline

• Kubernetes controlled elasticity — Easily orchestrated, grows and scales application security along with Kubernetes pods, including auto-learned policies and configuration settings.
• Automation at the speed of development — Application programming interfaces (APIs) for integration with common tools for security provisioning of new services and applications, with a local management and reporting interface.
• TLS termination — End-to-end encryption is necessary to secure data integrity and avoid eavesdropping and man-in-the-middle (MITM) attacks. A single TLS termination at the host also eliminates spreading multiple certificates across third parties.
• Minimal footprint — Microservices are all about micro units; thus, the enforcement point in the data plane should be lightweight while the control plane (management, analytics and learning algorithms) is integrated into the environment independently.

Quality of Protection

• Extensive security — Application protection today goes beyond the OWASP Top 10, so a good WAF needs to accurately detect malicious bot activity, secure APIs and mitigate denial-of-service attacks.
• Effective security (zero-day protection) — Negative and positive security models are necessary to protect against known and unknown threats, thus maximizing security and minimizing false positives.
• Adaptive security — Immediate detection of new and modified applications in the CI/CD pipeline isn’t enough and must be followed by automatic generation and optimization of security policies.
• Data leakage prevention — Make sure data that is being shared externally is protected. Credit card and Social Security numbers must be masked, cookies must be encrypted, and scrapers should be misled with fake data.

Supplementary Requirements

Endorsed technology — Multiple firms evaluate technology solutions, including ICSA, NSS, Forrester and Gartner. Don’t take our word for it — check it for yourself.

Comprehensive reporting and analytics — Visibility to both development, security and operations (DevSecOps) and security teams via integration with common tools and platforms like elastic Kibana, Grafana, Prometheus, among others.

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The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project’s maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare.

Originally developed at the National University of Singapore, SINGA joined ASF’s incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.

The success of deep-learning models has been driven by the use of very large datasets, such as ImageNet with hundreds of thousands of images, and complex models with millions of parameters. Google’s BERT natural-language model contains 300 million parameters and is trained on nearly 3 billion words. However, this training often requires hours, if not days, to complete. To speed up this process, researchers have turned to parallel computing, which distributes the work across a cluster of machines. According to Professor Beng Chin Ooi, leader of the research group that developed SINGA:

It is essential to scale deep learning via distributed computing as…deep learning models are typically large and trained over big datasets, which may take hundreds of days using a single GPU.

There are two broad parallelism strategies for distributed deep-learning: data parallelism, where multiple machines work on different subsets of the input data, and model parallelism, where multiple machines train different sections of the neural-network model. SINGA supports both of these strategies, as well as a combination of the two. These strategies do introduce some communication and synchronization overhead, required to coordinate the work among the machines in the cluster. SINGA implements several optimizations to minimize this overhead.

Acceptance as a top-level project means that SINGA has passed several milestones related to software quality and community, which in theory makes the software more attractive as a solution. However, one possible barrier to adoption is that instead of building upon an existing API for modeling neural networks, such as Keras, SINGA’s designers chose to implement their own. By contrast, the Horovod framework open-sourced by Uber allows developers to port existing models written for the two most popular deep-learning frameworks, TensorFlow and PyTorch. PyTorch in particular is the framework used in a majority of recent research papers.

ASF has several other top-level distributed-data processing projects that support machine-learning, including Spark and Ignite. Unlike these, SINGA is designed specifically for deep-learning’s large models. ASF is also home to MXNet, a deep-learning framework similar to TensorFlow and PyTorch, which is still in incubator status. AWS touted MXNet as its framework of choice in late 2016, but MXNet still hasn’t achieved widespread popularity, hovering at just under 2% in KDNugget’s polls.

Apache SINGA version 2.0 was released in April, 2019. The source code is available on GitHub, and a list of open issues can be tracked in SINGA’s Jira project. According to ASF, upcoming features include “SINGA-lite for deep learning on edge devices with 5G, and SINGA-easy for making AI usable by domain experts (without deep AI background).

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