IoT Design Patterns – Edge Code Deployment Pattern

Development with traditional software architectures and IoT architectures is quite different, particularly when it comes to the design patterns. To put it clearly, the detail and abstraction levels in IoT design patterns vary. Therefore, for creating high-quality IoT systems, it is important to assess various layers of design patterns in the internet of things.

Design patterns are used to add robustness and allow abstraction to create reusable systems. Keep in mind that design involves working with technical information and creativity along with scientific principles for a machine, structure, or system to execute pre-defined functions, enabling maximum efficiency and economy.

IoT helps to integrate the design patterns of both the software and hardware. A properly-designed application in internet o things can comprise of microservices, edge devices, and cloud gateway for establishing a connection between the Internet and the edge network so the users are able to communicate via IoT devices.

Development for configuring systems and linking IoT devices come with an increased level of complexity. Design patterns in IoT tackle issues to manage edge applications starting from initialization and going towards deployment. In developing IoT applications, edge deployment pattern is one of the most commonly used design patterns.

Edge Code Deployment Pattern


How to make sure the developers are able to deploy code bases for several IoT devices and ensure that they achieve the required speed. Similarly, there are also concerns regarding the security loopholes of the application. Additionally, it includes how to configure IOT devices while not worrying about the time-consuming phases of build, deployment, test, and release.


One of the primary factors for deploying a portion code is maintainability for deploying IoT devices which are remotely based. When developers resolve bugs and enhance the code, they want to add to ensure that this updated code is deployed to its corresponding IoT devices as soon as possible. This assists with the distribution of functionality across IoT devices. After some time, the developers may be required to perform reconfiguration of the application environment.

Let’s consider that you are working on an IoT system which uses billboards to show advertisements in a specific area. Throughout the day, your requirements include modifying the graphical and textual elements on the screen. In such a case, adaptability and maintainability emerge as two of the toughest challenges where the developers are required to update and deploy the code to all the corresponding IoT devices simultaneously.

Often networking connectivity slows down the internet of things ecosystem. To combat this dilemma, you can incorporate the relevant changes rather than proceeding the complete application upload with a network that is already struggling to maintain performance.

Developers are required to consider programming as their primary priority and search for the right tools which can assist with their development. It is important to ensure that the IoT devices’ code deployment tools remain transparent so it can help the developers. This can assist in achieving a deployment environment that is fully automated.

Subsequently, the safety of operations is also improved. As discussed before, your development pipeline should be aimed at building, deploying, testing, releasing and distributing the application for the devices in the internet of things ecosystem.

For testing, you can use the generated image with an environment that resembles the production environment and initiate your testing. After the completion of the tests, the IoT devices take out the relevant image. This image is derived out from the configuration files, the specification of the container, and the entire code. One more aspect is that there is a need for a mechanism which can help coders to rollback their deployment to a prior version so they do not have to deal with outage—something which is quite important for the IoT devices.

Additionally, for the new code requirements, the deployment stage has to contain and consider configurations and dependencies of the software. For this purpose, they need reconfiguration for the environment of the application and the entire tech stack via safely and remotely to maintain consistency.


Since in recent times developers are extensively using version control systems, it can help with deployments as well. Today, Git is heavily used by developers for maintaining versioning and sharing code. Git can serve as the initial point for triggering the building system and proceed with the deployment phase.

Developers can use Git for pushing a certain code branch at the server’s Git repository which is remotely based so it gets alerts about the software’s new version. Afterward, they can use hooks for triggering the build system initiating the following phase where they can deploy the code in the devices. A new Docker image is built by the build server which adds the image’s newly-created layers. They are received by the central hub of Docker so the devices can pull. With this strategy, a developer can use any version control system like Git to deploy code when the IoT devices are distributed geographically.

After regular intervals, these devices can inquire the central hub or registry to see if there are any new versions. Similarly, the server itself can alert the devices of a new image version release. Afterward, the newly-created image layers are pulled by the IoT devices where they generate a container and make use of the new code.

To summarize, after there are changes in the code, Git is used for commits and pushes. Subsequently, a freshly-built Docker image is created and moves to the IoT devices which then makes use of the image for generating a container and utilizing the code that has been delayed. Each commit starts the deployment pipeline and modifies the source code—thereby it is published for all the devices.


IoT Design Challenges and Solutions

The development of the internet of things architecture is riddled with various challenges. It is important to understand that there is a major difference between designing desktop systems and web applications in comparison to developing an IoT infrastructure as the latter has different hardware and software components. Hence, you cannot use the same traditional approach with your IoT applications which you have been using in the past with web and desktop software. What you can do is that consider the following IoT design challenges and solutions.

1.   Security

One of the key considerations in an IoT ecosystem is security. Users should have trust in their internet of things equipment which can help to share data easily. The lack of secure design means that IoT devices can encounter different types of security vulnerabilities in all of their entry points. As a result of these risks, private and business data can be exposed which can lead to compromise the complete infrastructure.

For example, in 2016, Mirai first arrived on the internet. Mirai is a type of botnet which went on to infect the IoT devices of a major telecommunications company in the US: Dyn. As a consequence, a large number of users were disconnected and they were without any internet connectivity. DDoS was one of the key hacking strategies which were used by hackers.


A considerable portion of the responsibility to secure IoT devices falls into the hands of the vendors. IoT vendors should incorporate security features in their devices and make sure to update them periodically. For this, they can use automation to perform regular patching. For instance, they can use Ubuntu in tandem with Snap which can help in a quick update of devices. These atomic styles assist developers in the writing and deployment of patches.

Another strategy is to ensure that DDoS attacks are prevented. For this, you have to configure routers so they can drop junk packets. Similarly, irrelevant external protocols like ICMP should be avoided. Lastly, a powerful firewall can do wonders and make sure to update the server rules.

2.   Scalability

By 2020, Cisco predicts that there will be around 50 billion functional IoT devices. Therefore, scalability is a major factor to handle such an enormous number of IoT devices.


For scalability, there are many IoT solutions which are not only scalable but are also reliable, flexible, and secure. For instance, one of the most common data management infrastructures is Oracle’s Internet of Things. This solution provides many efficient services that help with the connection of a large number of IoT devices. As Oracle is known to be a massive ecosystem with different services and products integrating into its architecture, thus they can help to fix a wide range of concerns.

For mobile development in your IoT ecosystem, you can use Oracle Database Mobile Service—a highly robust solution that helps to create a connection between embedded devices and mobile apps while fitting all the scalability requirements. There is also an option to use a scalable database like Oracle NoSQL Database which can offer you a chance to work on the modern “NoSQL” architecture.

3.   Latency

Latency is the time period which data packets take to move across the network. Usually, latency is measured through RTT: round-trip time. When a data packet goes back and forth from a source to its destination, the time it requires to do this is known as RTT. Milliseconds are needed to measure the latency of data centers where the range is fewer than 5 milliseconds.

IoT ecosystem usually employs several interconnected IoT devices at once. Thus, the latency increases as the network become heavier. The cloud is seen as the edge of the network by the IoT developers. It is necessary to understand that latency issues can affect even routine IoT applications. For instance, if you have an IoT-based automation system in your home and you turn on a fan then latency issues related to cloud processing, gateway processing, wireless transmission, sensing, and internet delivery can arise.


The latency issue is quite complex. It is imperative that businesses must learn the management of latencies if they plan to use cloud computing effectively. Distributed computing is one of the components which raises the cloud latency’s complexity. Application requirements have changed. Rather than using a local infrastructure for storage, services are distributed internationally. Additionally, the birth of big data and its tools like R and Hadoop are also boosting the distributed computing sector. Internet traffic has made latencies dependent on such scale that they find it hard to utilize similar bandwidth and infrastructure.

Another issue which plagues the developer is the lack of tools that can help with the measurement of the latest applications. Traditionally, connections over the internet were tested via the traditional ping and traceroute. However, this strategy does not bode well today as ICMP is not required for modern-day applications and networks in IoT. Instead, they need protocols like HTTP, FTP, etc.

By traffic prioritization and focusing on QoS (Quality of Services), you can address cloud latency. Before the birth of modern cloud computing, SLAs (Service Level Agreements) and Quality of Services were used to make sure that traffic prioritization was done well and ensure that latency-sensitive applications can use the suitable resources for networking.

Back-office reporting can force applications in accepting decreased uptime but the issue is that a lot of corporate processes cannot sustain the downtime because it causes major damage to the business. Therefore, SLAs should concentrate on specific services by using performance and availability as metrics.

Perhaps, the best option is to connect your IoT ecosystem with a cloud platform. For instance, you can use Windows Azure which is quite is powerful and robust, and can particularly serve those businesses which plan to develop hybrid IoT solutions—in such infrastructure on-premises resources are used for the storage of a considerable amount of data while cloud migration help with other components. Lastly, the collocation of IoT components to third-party data centers can also work out well.

What Should You Know Before Implementing an IoT Project?

By the end of 2019, more and more enterprise IoT projects are expected to be finished. Some of them are in the stage of proof of concept while there are some projects which ended badly. The reason behind this is that project managers were late to understand that they were fast-tracking the implementation of the IoT projects without thinking much. As a consequence, they were left to regret not consulting and analyzing properly.

Different industries and businesses use the internet of things for various applications, however, IoT fundamentals are unchangeable. Before implementing an IoT project, consider the following factors.

Cultural Shift

Organizational and cultural shift emerge as one of the leading issues in the internet of things. For example, take the example of a German cleaning manufacturer: Kärcher. The company’s digital product director, Friedrich Völker, gave a brief explanation of how the company was able to address this issue during its release of an IoT-based fleet management project. He explained that their sales team was struggling in marketing and advertising the software and virtual offerings of the internet of things as they dealt with their customers.

After some time, the sales department refrained from concentrating on a one-off sale. They instead focused their efforts on fostering relationships with the customers to get input on the ongoing performance of the IoT equipment. As a result, they achieved success.

Initiatives related to the internet of things are usually included in the digital transformation of companies which often needs adoption of the agile-based methodology along with the billing procedures for offering support to either pay-per-use based billing or subscription-based customer billing. Therefore, always commit to the efforts of change management in the organization and make use of the agile approach.

Duration of IoT Projects

Businesses need to understand that the implementation of the internet of things needs a considerable amount of time. There are examples where the IoT implementation from the development of the business case to the commercial release took less than a year—the quickest time was 9 months. On average, expect an IoT project to run for at least 24 months.

There are many reasons for such a longer duration of IoT projects. For instance, sometimes the right stakeholders do not have the buy-in. In other cases, there can be a technical problem such as not using an infrastructure which provides scalability support.

Profitability cannot be expected for the initial years; many companies are concentrating on the performance of their internet of things solutions instead. Thus, you have to ensure that the stakeholders are patient and create smaller successes which can provide satisfaction to both the senior management and the shareholders.

Required Skills

Development of end-to-end applications in the internet of things needs a developer to have an extensive set of skills such as cloud architecture, data analytics, application enablement, embedded system design, back-end system integration like ERP, and security design,

However, IoT device manufacturers do not have much experience of the technology stack in the internet of things like AMQP or MQTT protocols, edge analytics, and LPWAN communication. Studies indicate that there is especially a wide gap of skills in the data science domain. To address these concerns, adhere to the following.

  • Map the gap of skills in your IoT project.
  • Make sure that your employees become the jack of all trades .i.e. do not limit them with a single domain—instead encourage a diverse skill set, especially with a focus on the latest IoT technologies.
  • Fill up your experience gap with the help of IoT industry experts so their vast expertise and experience can help you to provide a new level of stability in your project.


In this age, users are quite casual with the use of technology; they download and install an application on a smartphone within a few minutes and begin using them without any other thoughts. IoT adopters believe that IoT devices will provide a similar user experience.

On the contrary, one of the most time-consuming aspects in the development of the internet of things solutions is the protocol translation. For instance, in one case, the internet of things implementation for an industrial original equipment manufacturer (OEM) required almost 5 months to design all the relevant and mandatory protocol translations. It was only after such a prolonged time period that the IoT applications and equipment were able to function seamlessly. Therefore, make sure you can create a standardized ecosystem which falls in the scope of your industry and use case.


Not many people report it but a large number of IoT devices can generate scalability issues. When such an issue arises, device manufacturers cannot do much as the device is already released and sold in the market.

For instance, once a construction equipment manufacturer designed tidy dashboards for the remote monitoring of machines. After some time, the IoT infrastructure was revamped such that predictive maintenance and the hydraulic systems’ fault analysis could be performed. At this phase, it was first realized that the data model was not supported by the required processing capacity. Similarly, there were instances in which the processing power was weak and restricted the manufacturer into adding different functionalities.

While you should always begin small, your vision and planning should be grand from day one. Design your IoT with a modular approach and challenge your data model and hardware design.


Often, security is cut off from the development of IoT devices. This is because many consider security as merely an afterthought while embarking on a mission to create IoT technologies. However, device and data security have a prominent role in the development of the internet of things development.

For instance, some manufacturers of Connected Medical Devices use the services of an ethical hacker. This hacker looks for any possible security loopholes in the IoT project. To do this, they use a wide range of strategies for rooting IoT equipment, lift, penetrate, and alter its code.

Microsoft Azure with Augmented Reality

Microsoft has its hands full with AR (augmented reality). The release of Azure Kinect camera and HoloLens 2 headset indicates the intent of Microsoft to become a pioneer in the next big technology, augmented reality.

Microsoft Azure has been combining several technologies at once. It does not use every available tool to create a user experience for HoloLens or any other standalone device. Instead, it uses your models and fixes them for a particular physical location. When data is collected by Azure, you can use Google’s ARCore or Apple’s ARKit to access that data.

Microsoft’s new AR solutions have links which serve as a connection between the physical and virtual realms. Microsoft has termed them as spatial anchors. Spatial anchors are maps which apply to lock on the virtual objects for the physical world which is used to host the environment. These anchors offer links through which you can display a model’s live state across several devices. You can link your models to different data sources, thereby offering a view which can integrate well with IoT-based systems as well.

Spatial Anchors

Spatial anchors are intentionally made to function across multiple platforms. The appropriate libraries and dependencies for the client devices can be availed via services like CocoaPods while taking advantage of native programming language code as that of Swift.

You have to configure the accounts in Azure to ensure authentication for the services of the spatial anchors. While Microsoft is still using Unity, there are some indicators that it may support the Unreal Engine in the near future.

In order to utilize this service, it is necessary to first generate a suitable application for the Azure service. Partial anchors in Azure offer support to the mobile back-end of Microsoft so they can be used as service tools and the learning curve does not become too complex.

After you initiate and run an instance of the Azure App Service, you can use REST APIs to establish communication between your spatial anchors and client apps.

Basically, spatial anchors can be seen as an environment map to host your augmented reality content. This can include the use of an app to search for users in an area and then create its corresponding map. HoloLens and similar AR tools can replicate this functionality automatically. On the other hand, in some AR environments, you might have to process and analyze an area scan to construct the map.

It is important to note that anchors are generated by the AR tools of the application after which Azure saves them as 3D coordinates. Moreover, an anchor may have extra information attached to it and can use properties to assess the rendering and the link between multiple anchors.

Depending upon a spatial anchor’s requirement, it is possible to set an expiration date if you don’t want them to remain permanent. After the expiration date is passed, users can no longer see the anchor. Similarly, you can also remove anchors when you do not want to display any content.

The Right Experience

One of the best things about spatial anchors is its in-building navigation. With linked anchors and the appropriate map, you can create navigation for your linked anchors. To guide users, you can include tips and hints in your apps like the use of arrow placement to indicate the distance and direction for the next anchor. Through the linking and placement of anchors in your AR app, you can allow users to enjoy a richer experience.

The right placement on the spatial anchors is necessary, it helps users to enjoy an immersive and addictive user experience or else they can get disconnected with the app or game. According to Microsoft, anchors should be stable and link with physical objects. You have to think about how they are viewed to your user, factor all the possible angles, and make sure that other objects do not obstruct access to your anchors. The use of initial anchors for definite entry points also decreases complexity, thus making it more convenient for users to enter the environment.

Rendering 3D Content

There are plans by Microsoft to introduce a service for remote rendering which will use Azure to create rendered images for devices. However, the construction of a persuasive environment requires a great deal of effort and detail. Hardware like HoloLens 2 may offer a more advanced solution but delivering rendering in real time is a complicated prospect. You will require connections which run on high bandwidths along with a service for remote service. This service is necessary so you can pre-render your images with high resolution and then deliver them to the users. This strategy can be reapplied across multiple devices where the rendering processing runs once after which it can be used several times.

Devices can be classified under two types: untethered and tethered. Untethered devices with low-level GPUs are unable to process complex images. As a consequence, the results limit image content and deliver a lesser number of polygons. Conversely, tethered devices fully use the capabilities of GPUs which are installed in workstations with modern, robust hardware and thus are able to display fully rendered imagery.

It has been a while since GPUs have become prominent in the public cloud scene. Most of the support which Nvidia GPU provides to Microsoft Azure is based on cloud-hosted compute on a large scale and CUDA. It provides multiple NV-class virtual machines which can be used with cloud-based visualization apps and sender hosts.

Currently, Azure Remote Rendering is still a work in progress and there has been no announcement to explain its pricing strategy. By leveraging the power of Azure Remote Rendering and using it with devices like HoloLens, you can use your portable devices to execute complex and heavy tasks and ensure that you can deliver high-quality images.

Practical Applications of Machine Learning in Software Delivery

The DevOps survey explains how certain practices help in creating well-performing teams. However, it also sheds light on the gap which is created between two teams—one which works with a quality culture and the other without one.  In order to bridge this gap, machine learning has been cited as one of the solutions. So, how can machine learning change the game?

Save Time in Maintenance of Tests

Many development teams suffer from false positives, mysterious test failures, false negatives, and flaky tests. Teams have to create a robust infrastructure for analytics, monitoring, and continuous delivery. They then use automated tests and use test-driven development for both their user interface and APIs. As a consequence, a lot of their time is spent on maintaining their tests.

This is where machine learning can be useful and automate such tests. For example, the auto-healing functionalities of mabl can be used for such purpose. Algorithms of mabl are optimized in order to pick the target element for which interactions are required for a journey.

Many factors and considerations are needed to create maintainable automated tests, however, the capability of user interface test in assessing the change and execute accordingly saves a considerable amount of time. It may be possible to apply these benefits for the other types of tests too. The automation of tests for the service or API level is significantly less taxing in comparison to the user interface; however, they also need maintenance along with the modifications of the application. For instance, machine learning can be required to choose new API endpoints parameters and place some other automated tests for covering them.

Machine learning is great at consuming large amounts of data and learn from it. The idea to assess and determine failures in tests for the detection of patterns is one of the best advantages of machine learning.  You may even get to find an issue prior to the failure of any test assertions.

More on Testing

You can work around your test and production code in such a way that along with information about errors, it can also log the information pertaining to an event’s failure. While this sort of information can be too big size for a human to make sense out of it, machine learning is not restricted by such limitations. Therefore, you can use it to create meaningful output.

Machine learning can assist in the design of reliable tests which saves up on time. For instance, it can detect anti-patterns in the code of test. Similarly, it can identify those tests which can be marked with a lower priority or identify those system modules which can be mocked so a test runs quicker.

Like mabl, concurrent cloud testing increases the speed of continuous integration pipelines. However, when end-to-end tests are run in a website browser, then their speed is slow in comparison to those tests which run in the browser’s place.

Testers use machine learning to get recommendations like which tests should be automated with regards to their importance or when to automatically create such tests. To do this, a production code’s analysis can assist to pinpoint problematic areas. One application of machine learning is a production usage analysis to discover user scenarios and automatically generate or recommend the creation of automated test cases for covering them. The optimization of time which is needed to automate tests can be replaced by an intelligent automation mechanism of the most necessary ones.

You can use machine learning to assess production use and see how to get the application’s user flows and collect information about the accurate emulation of production use for security, accessibility, performance, and another testing.

Test Data

The creation and maintenance of data which is similar to production consist of different automated tests. Often, serious problems fail to get detected in production as the problematic edge needs a data combination which is not replicable in standard test environments. Here, machine learning can locate a detailed, representative, and comprehensive production data sample, eliminate any possible privacy concerns and produce canonical test data sets which are required by manual exploratory testing and automated test suites.

You can equip your production code, log detailed events data, and configure production alerts and monitoring—this can assist in a quicker recovery. Decreasing MTTR (mean time to recovery) is a nice objective whereas low MTTR is linked with high performance. For domains where the level of risks is particularly higher like in real life critical applications, you may have to use exploratory testing to decrease the possibilities of failure.

Human Factor

While context differs, however, most of the time, it is not advised to experiment with all types of automated testing in types. Thus, human eyes and similar senses are required along with the thinking skills to assess and be informed about the types of risks which can hit the software in the future.

The automation of boring stuff is also necessary so it can help testers for the interesting testing. Among the machine learning applications, one of the initial for test automation is visual checking. For instance, screenshots of all the visited pages are used by mabl to create the visual models of the automated functional journey. It identifies those parts which must change like ad banners, date/time or carousels. Similarly, it rejects new modifications in these areas. It is used for the shrewd provision of alerts whenever visual differences are detected for the areas which must look the same.

In case you have executed visual checking all by yourself through viewing a user interface website page in the production along with testing the same element, then you can understand how heartbreaking it can be. Thus, machine learning can help here to complete all the repetitive and tedious tasks.

7 Effective DevOps Tips to Transform the IT Infrastructure of Your Organization

DevOps refers to a group of modern-day IT approaches and practices which are designed to join operations and IT staff members (generally software developers) on a project with unforeseen levels of collaboration. The idea is to eliminate the traditional hurdles and barriers which have historically plagued cooperation between these departments, resulting in losses to an organization. Therefore, DevOps quickens the deployment of IT solutions. As a result, development cycles are optimized and shortened, saving money, time, staff, and other resources.

In the last few years, DevOps has commanded a strong presence in the IT circles. Despite its popularity as a useful approach, the domain lacks visibility and suffers from improper implementations. As a result, the operation and development departments are unable to leverage its maximum advantage. The integration of DevOps is needed in organizations where the IT leadership has its own strategy for its implementation.

As DevOps gains greater recognition, its community has managed to discuss the best practices which can help organizations improve collaboration and develop higher quality IT solutions. Following are some of the DevOps tips that can help you propel your IT department in the right direction through clean coding and optimized operations.

1.     Participation from the Concerned Parties

One of the founding principles of DevOps is to ensure that the operations personnel, the development professionals, and the support staff are facilitated to work together at regular intervals. It is important that all of these parties recognize the importance of each other and are willing to collaborate.

One of the well-known practices in the agile industry is the use of onsite customer where the agile development team operates the business by offering their support. Experienced professionals are known to adopt stakeholder participation practice. This practice recommends developers to expand their horizons and work closely with all the concerned parties other than the customer.

2.     Automated Testing

An agile software engineer is always expected to spend a considerable amount of time in quality programming along with focusing on testing. This is why automated regression testing is a recurring tool in the toolbox of agile developers. Sometimes, it is modeled as behavior-driven development or test-driven development.

One of the secret ingredients for the success of agile development is the fact that these environments are continuously run daily tests to identify issues quickly. Subsequently, they also receive rapid fixing. As a result, such environments are able to attain a greater degree of software quality in contrast to the traditional software approaches.

3.     Addition of “I” in Configuration Management

Configuration management is a set of standard practices which is required to monitor and manage changes in the software lifecycle. Previously, configuration management was ineffective because it was too limited. DevOps has allowed adding “I” in the CM equation, which has produced integration configuration management. This means that along with the implementation of CM, developers are also analyzing problems related to production configuration which originates between the infrastructure of the company and the proposed solution. This is a key change because developers were not too concerned about such variables in the past and only focused on CM in terms of their solutions.

DevOps promotes a fresh brand of thinking which targets enterprise-level awareness and offers visibility to a more holistic picture. The ultimate goal of integrated configuration management is to ensure that developer teams are equipped with the knowledge about all the dependencies of their solutions.

4.     Continuous Integration

When a project’s development and validation undergoes automated regression testing along with optional code analysis (after the code is updated via version control system), the approach is labeled as continuous integration. CI evokes positive responses from agile developers working in a DevOps environment. With support from CI, developers have been able to design a working product with gradual and regular coding while any code defect is promptly addressed through immediate feedback.

5.     Addition of I in DP

Traditionally, deployment planning was done with cooperation from the operation department of organizations whilst at times the release engineers were also consulted. Those who have been in the industry for a long period of time are compliant with this planning from the assistance received from active stakeholder participation.

In DevOps, one is quick to come to the understanding that a cross-team is necessary in deployment planning so the staff from the operations department is easily able to work with each of the development teams. Such practice may not be anything new for the operations staff, though development teams would find it the first instance due to their restrained nature of duties.

6.     Deployment

The methodology of CI is further enhanced through continuous development. In CD, whenever integration is met with success in a single sandbox, the modifications are sent to the upcoming sandbox as a sort of promotion. The promotion can only be stopped when the modifications require verification from people who serve the operations and development.

With CD, developers are able to decrease the time duration elapsed between the identification of a new feature and its deployment. Companies are becoming more and more response thanks to CD. Though, CD has raised some eyebrows for operational risks where errors maybe originated during the production due to the negligence of the developers.

7.     Production

In high-scale software environments, often software development teams are engaged in updating a new release of a product which is already established .i.e. it is in production. Hence, they are not only required to spend time on the newer release but also handle any incoming issues in the production front. In such cases, the development team is effectively the third level of application’s support as they are expected to be the third and last set of professionals to respond to any critical production issues.

Final Thoughts

Do you work with enterprise-level development?

In this day and age, DevOps is one of the most useful tools for large organizations. Follow the above-mentioned tips and improve the efficiency of your IT-related solutions and products within a short period of time.

What Is a Recommender System and What Are Its Types?

Recommender systems were conceptualized due to the growing interactions and activities of users on the internet. Online spaces allow users to freely indulge in their favorite activities. For instance, consider IMDB (Internet Movie Database). Binge watchers visit it and click a rating out of 10 to offer their insights on the movie’s quality. This approach gathers feedback through “ratings.” Other metrics for feedback include the browsing of a term which calculates the popularity of a product.

These metrics are used by organizations in recommender systems to determine consumer habits. The objective is to enrich the user experience. In the nomenclature of recommender systems, a product is an “item” and the individual who uses the recommender systems is a “user.”

Recommender systems rely on the concept that there is a strong correlation between the activities of a user and an item. For instance, if a user likes to purchase graphic cards for gaming, then it is more likely that their other activities would be to purchase a gaming mouse, headphones, or any other gaming-related equipment. Thus, their choices can be used to show them more and more relevant recommendations while companies are able to provide unmatched levels of personalization.

Types of Recommender Systems

Recommender systems are classified under the following categories.

Collaborative Filtering

In collaborative filtering, when several users provide their feedback, for instance in the form of ratings, then their “insights” are computed to generate recommendations. The resulting ratings matrices of collaborative filtering are known to be sparse. For instance, suppose there is a movie website in which users assign ratings to reflect their likes/dislikes. Now, if the movie is a popular one, like The Shawshank Redemption, then it is expected to receive a massive number of ratings.

However, each movie has a limited audience. The audience that has seen and rated the movie is grouped as observed or specified ratings while those who have not watched the movie are labeled as unobserved or unspecified ratings.

Collaborative filters work on the foundation that the unobserved ratings may also be imputed. This is done by using the high correlation between items and users. For instance, consider two users Phil and Dan. Both of them like the movies starring Leonardo Di Caprio, and therefore share identical taste. When their ratings get too similar, then the model uses it to calculate their recommendations. This means when there is an item in which only Phil has submitted a rating, then Dan’s rating could be estimated through Phil’s choice and therefore Dan is shown that movie as a recommendation.

Content Based Recommender Systems

When an item’s descriptive characteristics are analyzed and computed for the generation of recommendations, then it is likely that the recommender system is based on content. The term here is “descriptions.” Such systems aggregate the purchasing behaviors and ratings which are recorded from the users and mixed with an item’s description (content).

For instance, considering a movie website, Mark has given a positive rating to the movie “Sixth Sense.” However, data from other users is not available so collaborative filtering is inapplicable. Instead, the recommender system can use content-based methods to go through the description of the movie, like its genre (suspense). Therefore, the system would recommend similar suspense-based movies to the user afterward.

In such systems, the ratings and descriptions of the items are employed as training data for the generation of a regression model or classification (user-specific). Whenever users are assessed, the training data matches their buying history with the description of items.


In scenarios where users are not too active with the purchase of items, knowledge-based systems exist as a useful tool. For instance, when a user buys real estate, then this purchase is commonly long-term. On a similar note, buying automobiles or luxury products are those activities which occur once in a while for a user. As you can understand, there are not enough ratings to offer the users with recommendations. Similarly, the description of such items also changes with the passage of time as the models and versions of these items may also change.

Knowledge-based systems do not consider ratings to generate recommendations. Instead, they attempt to find a link between the item descriptions and the requirements of a user. This approach makes use of “knowledge bases.” Knowledge bases store the similarity functions and set of rules that assist them in retrieving the right item.

For example, if a user intends to purchase a car, then the knowledge-based systems would provide recommendations based on the specifications of the car like the model year of the car, model type (sedan, convertible, hatchback etc), color, price, and other car-related attributes.

Demographic Recommender Systems

At times, the data from the demographics of users is used for designing classifiers which apply a mapping between the purchasing behavior and ratings of the user to their demographics. These systems thrive on the idea that demographic data can prove valuable in the recommendation of items. For instance, a website can show web page recommendations by identifying the interaction of a demographic with a specific web page. Often, a context is added in such systems to further improve their recommendations.

For example, consider a website which provides book recommendations to its users. The user attributes are gathered through an interactive dialog box. The system would account factors like the age, country, language, occupation, status (student/employed/owner), and other relevant factors. For instance, if a user is between the ages of 5-10, then its suggestions would gear towards children books. Likewise, if a user works as a finance analyst, then the system would use to show finance related books. On a similar note, if a user lives in France, then he/she would receive French book recommendations.

Final Thoughts

Recommender systems have become one of the most in-demand applications of machine leaning. Amazon, Netflix, Google, Facebook, every organization uses recommendation system to improve the user experience. Therefore, if you deal extensively with customers, then you might succeed in improving the productivity and efficiency of your business through the installation of recommender systems.