DevOps Tools for your Business

When it comes to software development, integrating the spheres of Development and Operations opens doors to a more refined perspective of software development. However, if you are new to the practices of DevOps, you may face certain problems initially regarding its understanding.

Not only this. Being new to DevOps practices will also make it difficult for you to select the right kind of tool for your team. To help you get the perfect tool, we have enlisted the 7 top DevOps tools that you can incorporate into your business operations.

The devops tools mentioned below are an amalgamation of automated building tools and application performance monitoring tools.

1) GRADLE

When it comes to a tool stack of DevOps, you will require a build tool that is trustworthy. Maven and Apache Ant may have been the frontrunners for quite a long time, but the appearance of Gradle in the year 2009 has changed a lot of things. Gradle has been enjoying a steady rise in popularity since then.

The versatility of Gradle is the main USP. A programmer can write codes in different programming languages like C++, Java, and Python among many others. It is important to note that Google selected Gradle as its official build tool for its Android Studio.

The best thing about Gradle has to be the facilitation of incremental building, as it helps to save a considerable amount of compiling time. The incredible number of configuration possibilities only adds to its advantages.

2) GIT

Git is a pretty favorite tool across developers in the software industry. It is a distributed source code management tool that is a favorite among open source contributors and remote teams alike. Git facilitates the tracking of progress for your development work.

You also get a lot of help by saving different types of your source code and also can refer to the older versions if needed. The ease of reference makes Git an excellent tool for experimentation because you can create separate sections and fuse them only when every part is complete.

For integrating your DevOps workflow with Git, you also need repositories where the members of your team slide in their work. Presently, Github and Bitbucket are the two most popular online Git hosting services.

Both the above services can be merged seamlessly with Slack, which helps each member get notified when somebody takes a particular action.

3) JENKINS

Jenkins is also one of the favorite tools for DevOps automation for many software developing teams. It is necessarily a CI/CD server with an open source which allows you to automate of different stages of your pipeline of deliveries. With a considerable plugin ecosystem, Jenkins enjoys a colossal bout of popularity.

The number of plugins on offer is about a 1,000 making the integration with many tools pretty flawless, be it Docker or Puppet. With the help of Jenkins, you can set and personalize your CI/CD pipeline as per your requirements.

Jenkins is also pretty easy to start working with as it runs on basic Windows, Linux and Mac OS from the start. Jenkins also makes it very easy to create and deploy the new code as soon as possible. The process helps in a straightforward measurement of every single step in your pipeline.

4) BAMBOO

Bamboo is a CI/CD server solution which provides the users with a lot of similar features that are available on Jenkins. While both Bamboo and Jenkins facilitate the automation of delivery pipeline, Jenkins is an open source service, but Bamboo is a premium service, which means you need to purchase it.

There is a price tag on Bamboo for sure, but it also comes in with some features which come out-of-the-box that need to be established manually in Jenkins. Due to pre-built functions, Bamboo has the upper edge. Bamboo also has lesser plugins because it already does many things right from the moment it is activated.

Bamboo is also able to get flawlessly integrated with the other Atlassian tools like Jira and Bitbucket. To sum up, Bamboo saves you from a lot of configuration time. The user interface is also more intuitive with auto-completion, tooltips and other useful features.

5) DOCKER

Since its launch in the year 2013, Docker has been the most popular container platform and is still improving. People consider Docker as one of the essential DevOps tools in existence. It is Docker which has made containerization a trend in the world of technology as it makes distributed development a reality and also deploys your apps automatically.

Docker isolates your applications into different containers to make them more secure and portable. The Docker apps are also OS and platform independent. These Docker containers can serve the purpose as a substitute for practical tools like VirtualBox.

6) KUBERNETES

Now Kubernetes is a tool which takes containerization to a whole new level. It works nicely with Docker and its other alternate tools as well. Kubernetes helps in the grouping of containers into more logical units.

When it comes to Kubernetes, the need to link your containerized apps with a single machine does not exist. You can designate this function to a collection of computer systems. The tool then automatically distributes and schedules the containers along the computer collection.

7) PUPPET ENTERPRISE

Puppet Enterprise is a configuration management platform that works on a cross-platform. The tool allows you to help in the management of your entire app infrastructure as a code. Puppet also gives the developers an open source tool for minor projects.

If you have the Puppet Enterprise, managing multiple teams and a whole load of resources becomes very easy. The most fantastic thing about Puppet Enterprise is that there are more than 5,000 kinds of modules and offers easy interlinking with the other DevOps tools as well.

Thus, we hope you this list of the devops tools helps you implement the best development and operations strategy. But to find out which DevOps tool works best for your team, you will need to experiment and test things. in the end, a tool’s performance boils down to your own goals and needs.

If you are still not sure which tool to opt for then let us assist you! Drop us a quick message about your business requirements.

Big Data applications in Disaster Management

Take out a page from history, and you will find that all those numerous policies have not been effective when it comes to rescuing people who are in the middle of a horrifying disaster. As innovations are constantly evolving, it’s time that administrations should focus more to include various Big Data technologies to help in the prediction of disasters and their relief work.

Great innovations like the Internet of Things (IoT) have become more regular today, which was not the case two decades ago. With the frequency of natural disasters increasing, the advancement in ways of communication through this technology has led to a considerable reduction in the number of casualties as well as injuries.

Agencies like NASA and National Oceanic and Atmospheric Administration (NOAA) have used big data technologies for the prediction of these natural disasters and then coordinate with the response personnel in cases of emergency. This technology has also been necessary for the agencies to shortlist a typical disaster response by taking down the locations of staging a rescue location and evacuation routes.

Also, agencies around the storm impact zone use the machine learning algorithms to have an idea about disasters like storms and floods, and the potential damage they could cause.

Big Data and Disaster Management

Big Data technology is a great resource that has been continuously proving its mettle in disaster relief, preparation, and prevention. Big Data helps the response agencies by identifying and tracking populations such as elder groups of people, regions where there is a large concentration of children and infants etc.

Big Data systems help in the purpose of coordinating with the rescue workers to identify the resources which could provide support and do some logistic planning in such emergency cases. The facilitation of real-time communication is also an added advantage in disasters because the use of this technology can forecast the reactions of citizens who will be affected.

Big data systems are now in the stage of growth with an acceleration rate with studies saying that 90% of data in the world was generated within the previous two years, which is simply huge. All this data helps the manager of emergency units make better-informed decisions at the time of a natural disaster.

The reports that are generated consistently prove to be a massive benefit for disaster response management by combining the data used for mapping geographical records and imagery that is real-time. They also give responders information regarding the status in affected areas, providing them a constant stream of real-time data in cases of scenarios which have emergency written all over them.

Benefits of Big Data

Big Data technologies are undoubtedly an important aspect to tackle natural disasters and make emergency responses very efficient.

However, there are a few broad benefits that are explained below with appropriate instances.

• Crisis Mapping

Nairobi’s non-profit data analysis community known as the Ushahidi, created an open-source platform of software to gather information. This technology works on a mapping platform which was first developed in the year 2008, analyzing the areas that became violent right after the Kenyan presidential elections.

Information at that particular time came through social media and many eyewitnesses. Their team members then put up the same information on a Google map that was interactive, helping the residents get cleared of danger.

The same technology was used again in the year 2010 when Haiti was jolted through an earthquake, proving integral in saving the lives of numerous citizens who were there in the region.

• Bringing loved ones and families closer

Facebook and Google are genuinely the present leaders in technology, and they too have invested in the development of some advanced resources which have their benefits during the time of natural disasters. Huge online systems have been deployed by them which enable the members of a family to connect again after separation in times of emergency.

The “Person Finder” application by Google was released right after the Haiti earthquake for helping people connect with their family members. The platform works on the function of people entering information about the missing persons and also reconnect with them at the time of a disaster.

• Prepare for emergency situations

Systems working on Big Data are continually making it better for the agencies to predict or forecast when a particular disaster can happen. The agencies work to ensure a combination of data collection, notification platforms and scenario modeling in forming great disaster management systems.

The residents give out household information which agencies use for the evaluation and allocation of resources at the time of natural disasters. For example, these citizens share information that can be lifesaving, such as the presence of family members that have physical problems inside the household.

The United States is in constant need of scientists who could work with the technologies that can help predict and save lives during a natural disaster. 

A considerable portion of company leaders is of the opinion that a shortage in the number of data scientists is making it pretty tricky for their enterprises for surviving a marketplace which is highly competitive. As the apparent result, firms that succeed in getting good IT people to perform much better due to sheer talent as compared to their rivals.

If the analysis of forecasters is to be believed, the companies in the United States will be creating close to around 500,000 jobs for data scientists who are very talented by the year 2020. The current pool of these scientists, however, points out the availability of only 200,000 of such scientists presently. It can just be good news as it provides new opportunities for all aspiring data scientists in the future.

Agile Methodology Tools in Business

Timely delivering projects under a defined deadline and a set budget is a priority for those companies who wish to maintain their credibility, reputation, and prestige. Projects that get delayed give a hard time to the enterprises throughout their hierarchy because late project delivery has a significant impact on the morale, level of productivity and focus as well. To make matters worse, incorrect implementation of agile methodology might force the employees to leave the company due to excessive stress.

In such a stressful situation, the single best thing an employer can do is to take a step in the right direction at agile methodology, which is where the real tools of project management enter to play. The tools help in the identification of the actual status of a project, the expected tenure of a project and all its practical applications.

Entering into the world of project management, one is a witness of the importance of flexible working methods while also ensuring the implementation of futuristic and latest techniques for gathering the results quickly. There are some project management tools which come in handy to assist in the implementation of Agile project methodology of management.

The eight best tools which we have for you to choose from are:

1. Trello

One of the most widely utilized tools of project management, Trello is renowned for its straightforward user interface (UI) and easy usability. The functioning of Trello can be figured out even by a beginner who does not have too much knowledge in the field of project management.

Trello gives you cards along with dragged columns. The primary columns are three of them that include To Do, Doing, Done. Pulling the map to the appropriate box involves the rest of the tool to plot and create new columns, a rapid and simple procedure.

The cards are objects which can be assigned to resources that are relevant and include the estimation, completion process as well as delivery dates of the projects underway. The reputation of Trello is evident from the fact that even Twitter makes the use of Trello.

2. Visual Studio Team Services (VSTS) 

If you love using Microsoft Stack, VSTS is the perfect tool for your needs. The device facilitates easy integration with Visual Studio, helping manage a technical project with maximum ease. Until five users, the option of using VSTS is free and some premium features that can be purchased. The best feature of VSTS is the mechanism to trace any changes in the code which is the best thing a developer can ever hope for.

3. JIRA 

When you talk about authenticity, Jira is that tool which lives up to your expectation in project management and is known for being the best tool for tracking the records of jobs done through Agile management. Be it small businesses, enterprises or big organizations, Jira is ideal for business of all sizes.

Just like Trello, columns and tickets are there for you to display the different phases of your work. These tickets can be made and then be attached to a resource. When you complete a sprint, the performance of each can be measured through pie charts and graphics representations too.

4. AXOSOFT 

It is a software for an Agile project which is helpful in the identification of bugs in the project and then taking up an accurate Scrum framework to plan these projects. Axosoft has many tools which make the developers work conveniently and create features which are under the budget, on the right schedule and free of bugs.  

Agile followers are in love with Axosoft because of the way this software helps business through the creation of an Agile workflow. The progress report of each is very transparent, and Axosoft also keeps it centralized which ultimately results in practicing Agile methodology to a maximum extent in any team.

5. ASANA 

Asana is one among the best task managing software, and facilitates a team for planning, sharing and tracking the advancement of a project with the mapping of every resource’s performance within the organization.

The interface is pretty easy. You need to create a workplace, add the projects required for completion. It is easy to allot, track and organize the tasks thereafter. You can also add up notes, comments, and tags to be clear and expressive with the motto.

6. Zoho Sprints 

Zoho Sprints assigns you the authority for creating backlogs through a drag and drop feature. You can also stretch the stories of individual users with priorities, which is an added feature other than allotting tasks to a team.

Every work item can be noted duly in a time sheet that has budget control measures like the billable and non-billable hours for a particular piece of the project.

7. WRIKE 

Wrike tool has dashboards, customizable workloads, and charts which boost a project in flowing freely. There are a lot of updating options where all kinds of scattered information that rests on your mail, images, and documents can be easily accessed. Simply put, WRIKE helps in streamlining the workflow that is relevant to the timely completion of a project.

WRIKE also features the collection of necessary information from the cloud, sending of emails and also seamlessly merges with applications like JIRA and Salesforce.

8. Velocity Chart 

This tool helps to have an idea about the value that is generated in every single sprint, helping you to estimate the amount of work which will be completed in subsequent runs. In other words, you can easily measure the velocity of your team’s work.

The Velocity Chart adds up the estimates for every complete and incomplete story. These estimates can be on factors like hours, business value and any other factors that can be assigned to a numerical value.

If you wish to include Agile methodology in your project management practice, the eight tools that we listed above can prove to be crucial to have quick and efficient project management. If you would like to implement agile methodologies to your project, you can contact us here.

IoT Trends in 2019

Did you know that in 2008 there were more things connected to the internet than people?

Don’t drop the jaw yet, the more interesting fact is by 2020 this number will touch the score of 50 billion. Also, the profits which are expected from this investment amounts to a whopping $19 trillion.

The internet of things (IoT) has re-defined the technological landscape in the last decade beyond our imagination. It has majorly aided us in improving the productivity of our routine tasks. This fact explains the reason as to why there is a steady rise in the number of connected gadgets in workplaces and homes across the globe. Knowing all that, the significance of IoT doesn’t demand any further explanation. So, let’s skip directly to the anticipated IoT trends that will rule in 2019.

1. Edge Computing

Edge computing means a method of distributed computing that is performed on distributed smart or edge devices instead of computation done in a centralized cloud environment. Edge computing reduces the cloud dependencies and data transfer volumes that provide extra agility and flexibility for business. It has a major effect on the industries where the decisions are based significantly on complex real-time data analysis and where there is restricted cloud connectivity.

The industries that depend on complex real-time data analysis are security, manufacturing, and public industry and the industries where cloud connectivity is mostly restricted are the logistics and shipping industry.

2. Greater stress on the security at endpoint

It’s no brainer that IoT gadgets are susceptible to hacks and security breaches. The greater the number of IoT devices you have, the more you are at risk. This is a major drawback with this technology, but this is taken care of in 2019. By the end of this year, endpoint security will increase significantly.

Hardware manufacture companies like Cisco and Dell have taken initiatives for creating specific infrastructure for the smart devices that are expected to be more durable and secure. Also, soon the security vendors will cover the edge domain and provide endpoint security functionalities along with the present list of offerings like providing insights into network health, avoiding data loss, application control, whitelisting, and privileged user control.

Increase in the endpoint security will prove to be a key to transformation in the IoT sector. The lag in the security of the endpoint devices has actually subdued widespread adoption of this technology.

3. Expansion into health care and manufacturing industry

Smart beacons, RFID tags, and sensors are proof that the manufacturing industry has leaped into the future with technological advancement. This is like another industrial revolution that is about to change the landscape of the manufacturing industry. It is anticipated by market analysts that the IoT devices will double between 2017 and 2020 in the manufacturing industry.

These devices are going to turn the tables for all the industry-specific processes like production, supply chain management, logistics, packaging, collection, distribution, and development. Manufacturers can seize this opportunity to enhance production numbers, manage inventory more effectively, avoid unwanted delays, and most of all minimize the equipment downtime. This industry will witness the next level of development and upside streak in the year 2019.

Apart from the manufacturing industry, the  IoT technology has significantly covered the healthcare industry as well. As per research conducted by Aruba Networks, 60% of healthcare organizations across the world have introduced IoT devices. The road to smart pills, Electronic Health Record (EHR), and personal healthcare management now seems an easy one.

4. The growth of consumer IoT industry

The siloed and narrow experiences offered by the smart homes clustered with the inability to function with other forms of services is a drawback that makes it difficult for the vendors to fetch a continued subscription from the users. To curb these issues, multiple industry players have come together to cater to several necessities of the users and form a one big lucrative subscription offering. These players include utilities, food, grocery companies, and insurance majorly. No doubt, the smart homes are about to become smarter in 2019.

5. Deeper Market Penetration of Connected Smart Cars

Say hello to the connected app that will show real-time diagnostic information about the car. All thanks to the IoT technology that has blown life in this concept of smart cars. This real-time diagnostic information of the smart cars includes not only the basic intel but also complex information such as oil level, fuel consumption, tire pressure and so on. The catch is, all this intel is available at your palm.

Feeling like Tony Stark from Avengers yet?

Well, it isn’t over! Beside diagnostic information, you will witness other IoT advancements as well in 2019. These advancements include connected apps, current traffic information, and voice search which are currently available in rudimentary forms.

6. A big welcome to the era of 5G

5G networks, the most awaited tech trend in the industry, is making its grand entry in 2019. 5G network will be the backbone of IoT technology by supporting the growing interconnectivity of the IoT devices. A high-speed 5G network will allow collecting, managing, and analyzing the data in real-time. Imagine a world where you won’t have to wait even for a minute!

The 5G network will soon become a reality of our lives and will significantly broaden the IoT market as well in the industries where real-time analyzing is very crucial.

Conclusion

In the coming years, IoT will become a part and parcel of our lives. The profounding impact that it has on our life currently, is revolutionary in all the aspects. The year 2019, will surely bring some major strokes on the IoT landscape, with the 5G network already in the pipeline. From smart homes to smart cars to the way business is done, everything around us is going through a major transformation and for good.

Kubernetes in Big Data

Every organization would love to operate in an environment that is simple and free of clutter, as opposed to one that is all lined up with confusion and chaos. However, things in life are never a piece of cake. What you think and want rarely lives up to your choices, and this is also applicable to large companies that churn a massive amount of data every single day.

This is the point. Data governs the era we all live in. It is these data piles that prove to be a burden to a peaceful working process in companies. Every new day, an incredible amount of streaming and transactional data gets into enterprises. No matter how cumbersome it all may be, this data needs to be collected, interpreted, shared and worked on.

Technologies which are assisted by cloud computing offer an unmatchable scale, and also proclaim to be the providers of increased speed. Both of them are very crucial today especially when things are becoming more data sensitive every single day. These cloud-based technologies have brought us to a critical point that can have a long term effect on the ways which we use to take care of enterprise data.

Why Kubernetes?

Known for an excellent orchestration framework, Kubernetes has in recent times become the best platform for container orchestration to help the teams of data engineering. Kubernetes has been widely adopted during the last year or so when it comes to the processing of big data. Enterprises are already utilizing Kubernetes for different kinds of workloads. 

Contemporary applications and micro-services are the two places where Kubernetes has indeed made its presence felt strongly. Moreover, if the present trends are anything to go by, micro-services which are containerized and run on Kubernetes have the future in their hands.

Data workloads which work on the reliance of Kubernetes have a lot of advantages when compared to the machine based data workloads-

• Superior utilization of cluster resources
• Better portability between on-premises and cloud
• Instant upgrades that are selective and simple
• Quicker cycles of development and deployment
• A single, unified interface for all kinds of workloads

How Big Data entered the Enterprise Data Centers

To have an idea about the statement above, we need to revisit the days of Hadoop.

When Hadoop was first introduced to the world, one thing soon became evident. It was not capable enough to manage the emerging data sources effectively and the needs of real-time analytics. The primary motive for building Hadoop was to enable batch-processing. This shortcoming of Hadoop was taken care of with the introduction of analytics networks like Spark.

The ever-increasing ecosystem did take care of a lot of significant data needs but also played an essential role in creating chaos in the outcome. A lot of applications that worked with analytics tended to be very volatile and did not follow the rules of traditional uses. Consequently, data analytics applications were kept separately from other enterprise applications.

However, this is the time we can surely say that things headed in the right direction where cloud-native technologies that are open sourced like Kubernetes, prove to be a robust platform to manage both the applications as well as data. Also, explanations are under development which helps to allow the workloads of analytics to run on IT infrastructures which are containerized or virtualized.

During the days of Hadoop, it was data locality which acted as a formula that worked. The data was made available for distribution and then close for computation. In today’s scenario, storage is getting decoupled by computer. From the distribution of data to the delivery of access, the merging of these data analytics workloads and on-demand clusters based on Kubernetes is also on us.

Shared storage repositories are vital for managing workload isolation, providing speed, and enabling the prohibition of data duplication. This helps the teams leading analytics in setting up elaborate customized clusters which meet their requirements without recreating or moving larger sets of data.

Also, data managers and developers can raise queries to structured and unstructured data sources without the assistance of costly and chaotic data movement. The time taken for development gets accelerated, helping the products to enter into markets quickly. This efficiency which brought through a distributed access in a shared repository for storage will result in lesser costs and thorough utilization.

Unlocking Innovations through Data

With the use of a shared data context for isolation of multi-tenant workloads, the data is unlocked and easy to access by anybody who wishes to utilize it. The data engineers can also variably provide these clusters with the right set of resources and data. Teams on data platforms can strive for achieving consistency among multiple groups of analytics, while groups for IT infrastructure can be provided access to the clusters to use in the overall foundations which so far is being used for different traditional kinds of workloads as well.

Applications and data are ultimately getting merged to become one again, leading to the creation of a comprehensive and standardized source to manage both on the same infrastructural level. While this entire process might have used up a few years, today we have finally succeeded in ushering an era where companies can successfully deploy a single infrastructure for the management of big data and many other needed and related resources.

This is possible only because of open-source technologies, which are also based on a cloud system. There is no doubt that such techniques will continue to pave the way ahead, acting as a stepping stone for the evolution of more advanced and concise technologies in the future to come.

Machine Learning Technology in Facial Recognition

You will find it hard to believe, but it is entirely possible to train a machine learning system so that it can decipher different emotions and expressions from human faces with high accuracy in a lot of cases. However, implementing such training has all the chances to be complicated and confusing. This arises because machine learning technology is still at an early age. The absence of data sets which have the required quality are also tough to find, not to mention the number of precautions which are taken when such new systems are to be designed are also hard to keep up with.

In this blog, we discuss Facial Expression Recognition (FER), which we will discuss further on. You will also come to know about the first datasets, algorithms, and architectures of FER.

Images classified as Emotions

Facial Expression Recognition is referred to as a constraint of image classification which is found in the deeper realms of Computer Vision. The problems of image classification are the ones where pictures are assigned with a label through algorithms. When it comes to FER systems specifically, the photos tend to involve human faces, the categories being a specific set of emotions.

All the approaches from machine learning to FER need examples of training images, which are labeled by a category of a single emotion.

There is a standard set of emotions that are classified into seven parts as below:

1. Anger
2. Fear
3. Disgust
4. Happiness
5. Sadness
6. Surprise
7. Neutral

For machines, executing an accurate classification of an image can be a tough task. For us as human beings, it is straightforward to look at a picture and decide right away what it is. When a computer system has to look at an image it observes the pixel value matrix. For classifying an image, the system needs to organize these numerical patterns inside the image matrix.

The numerical patterns we mentioned above are variable most of the time, making it more difficult for evaluation. This happens because emotions are often distinguished only by the slight changes in facial patterns and nothing more. Simply put, the varieties are immense and therefore pose a tough job in their classification.

Such reasons make FER a stricter task than other image classification procedures. What should not be overlooked is that systems that are well-designed achieve the right results if substantial precautions are taken during development. For instance, you can get a higher accuracy if you classify a small subset of emotions that are easily decipherable like anger, fear, and happiness. The accuracy gets lower when the classification is done with large or small subsets where these expressions are complicated to figure out, like disgust or anger.

Common components of expression analysis

FER systems are no different than other modes of image classification. They also are using image preprocessing and feature extraction which then leads on to training on shortlisted architectures. Training yields a model which has enough capabilities to assign categories of emotion to new image examples.

Image pre-processing involves transformations like the scaling, filtering, and cropping of images. It is also used to mark information related to the photos like cropping a picture to remove the background. Generating multiple variants from a single original image is a function that gets enabled through model pre-processing.

Feature extraction hunts for the parts of an image that is more descriptive. It means typically getting information which can be used for indicating a specific class, say the textures, colors or edges as well.

The stage of training is executed as per the training architecture which is already defined. It determines a combination of those layers that merge within a neural network. Training architectures should be designed keeping the above stages of image preprocessing and feature extraction in mind. It is crucial as some components of architecture prove to be better in their work when used together or separately.

Training Algorithms and their comparison

There are quite a number of options which are there for the training of FER models, with their own advantages and drawbacks, which you will find to be more or less suited for your own game of reasons.

• Multiclass Support Vector Machines (SVM)

These are the supervised learning algorithms which are used for analysis and classification of data and are pretty able performers for their ranking of facial expressions. The only glitch is that these algorithms work when the images are composed in a lab with natural poses and lighting. SVM’s are not as good for classifying the images which are taken in the spur of a moment and open settings.

• Convolutional Neural Networks (CNN)

CNN algorithms use the application of kernels to large chunks of the image that is the input for a system. With this, a new kind of activation matrix called the feature maps is passed as the input for the next network layer. CNN helps to process the smaller elements of the image, facilitating ease to pick out the differences among two similar emotions.

• Recurrent Neural Networks (RNN)

The Recurrent Neural Networks apply a dynamic temporal behavior while classifying a picture. It means that when the RNN does the processing of an instance of input, it not only looks at the data from the particular instance but also evaluates the data which was generated from the previous contributions too. It revolves around the idea to capture changes between the facial patterns over a period, which results in such changes becoming added data points for further classification.

Conclusion

Whenever you decide to implement a new system, it is of utmost importance that you do an analysis of the characteristics that will exist in your particular situation of use. The perfect way of achieving a higher efficiency will be by training the model to work on a small data set which is in tandem with the conditions that are expected, as close as possible.

AI predictions to look out for in 2019

It is not a lie when we say that Artificial Intelligence or AI, is the leading force of innovation across all corporations on the globe. The market for Artificial Intelligence globally is on the rise. From a mere $4,065 billion in 2016, it is expected to touch a whopping $169,411.8 million by 2025.

According to the online statistics and business intelligence portal Statista, a significant chunk of revenue will be generated by AI targeted to the enterprise application market. With the advent of 2019 however, Artificial Intelligence is only expected to cross another threshold in its popularity. Let us look at the top predictions in AI for the year of 2019:

• Google and Amazon will be looked upon for countering bias & embedded discrimination in AI 

In fields that are so diverse as to include speech recognition, it is Machine Learning which is the formidable force of AI that enables the speech of Alexa, the auto-tagging feature of Facebook as well as the detection of a passing individual on Google’s self-driving car. When it comes to Machine Learning, existing databases of the decisions taken by humans help it to take appropriate decisions.

But sometimes even the data is not able to depict a clear picture of a group that is broad. This poses a problem because if the datasets are not appropriately and sufficiently labeled, capturing the broader nuances of the datasets is a difficult job.

2019 will surely witness companies who have products devoted to unlocking datasets that are more inclusive in structure, thus reducing the bias in AI.

• Finance and Healthcare will adopt AI and make it mainstream

There was a time when the decisions taken by AI relied on algorithms which could justify without too much fuss. Irrespective of the output whether right or wrong; the fact that it could explain decisions holds a lot of importance.

In services like healthcare, decisions from machines are a matter of life and death. This makes it critical to evaluate the reasons behind why a device rolled out a particular decision. The same applies to the field of finance as well. You should be aware of the reasons why a machine declined to offer a loan to a particular individual.

This year, we will see AI being adapted to facilitate the automation of these machine-made predictions and also provide an insight into the black box of such predictions.

• A war of algorithms between AI’s

Fake news and fake images are just a couple of handy examples of the ways things are moving ahead in terms of misleading the machine learning algorithms. This will pose challenges to security in cases where machine algorithms either make or break a deal, such as a self-driving car. So far, the only concern revolves around fake news, misleading images, videos, and audios.

More significant, consolidated and planned attacks shall be demonstrated in a very convincing way. This will only make it difficult to evaluate the authenticity of data and its extraction to be more precise.

• Learning and simulation environments to train data

It is true when we say that most projects revolving around AI require data of the highest quality with a set of great labels too. But most of these projects fail even without initiation as data that explains the issues at hand isn’t there, or the data which is present is very tough to label, thus making it unfit for an AI consideration.

However, deep learning helps to address this challenge. There are two ways to utilize the deep learning techniques even where the amount of data is pretty less than what is required.

The first approach is to transfer learning- this is a method where the models learn through a domain that is suitable with a large amount of data and then bootstrap the teaching at a different field where the data is very less. The best thing about transfer learning is that the domains are perfect even for different kinds of data types.

The second option is a simulation and the generation of synthetic data. The adversarial networks help out in creating data that is very realistic. We again consider the instance of a self-driving car. The companies producing these cars make practical situations which are focused on a lot more distance than the car will travel in reality.

This is why it is predicted that a lot of companies will make the use of simulations and virtual reality to take big leaps with machine learning which was previously impossible due to many data restrictions.

• Demand for privacy will lead to more spontaneous AI

With customers becoming more cautious at the prospect of handing their data to companies on the internet, businesses need to turn to AI and machine learning for access to such data. While this is a move that is still enjoying early days, Apple is already running some machine learning models on their mobile devices and not on their cloud systems, which is a depiction of how things are about to change.

It is assured that 2019 will see an acceleration in this trend. A more significant chunk of the electronic group encompassing smartphones, smart homes as well as the IoT environment will take the operations of machine learning to a place where it needs to be adaptive and spontaneous.

At GoodWorkLabs we are constantly working on the latest AI technologies and are developing machine learning models for businesses to improve performance. Our AI portfolio will give you a brief overview of the artificial intelligence solutions developed by us.

If you need a customized AI solution for your business, then please drop us a short message below:

How to become a JAVA Developer 

A lot of people know Java the programming language. What these people don’t know is that merely knowing this programming language is not going to be enough for you in the long run. You need to be very proficient in Java programming and coding if you aspire to create a functional and feasible application. Sitting around with the same level of knowledge is not going to help your case one bit. You can try to polish yourself more in Java programming if you wish to be the best Java programmer.

Java is simply the most popular programming language. A lot of Java developers are already there who have a good knowledge of the recent trends in technology with the willingness to learn the latest developments of Java, which include Java 8, JVM and also JDK 10.

If you want to get hired in a Java development company, you will have to present a considerable amount of difference when it comes to efficiency and skills from your end. 

Read on to know the 7 best tips that can help you become a better Java developer:

1) Learn JAVA 8

There are a lot of Java developers who are backed up with an experience of about 6 or 8 years, but still, they have not got to terms with using features of Java 8 like Lambda Expressions, different default methods and the Java Stream API.

If you can get a good grip on these features of Java 8, you already are ahead of the competition.

2) Good knowledge of Java API and libraries

Being one of the most solid programming languages out there, Java has the second biggest Stack Overflow community, which outlines a crucial role in the development of the entire Java ecosystem. Java APIs and Libraries constitute a big part of the ecosystem. Knowledge related to vital APIs and libraries, third-party libraries, and Java Development Kit is considered as an essential attribute for a good Java developer.

While the knowledge of every single API and all elements in the library is not expected from a Java developer, the refinements regarding crucial APIs and libraries should be there for sure.

3) Learn Spring Framework (Spring Boot)

Now, this is a platform that is essential for you as a Java developer without a doubt. The Spring framework allows a developer to create applications from some pretty old Java objects and is also very useful in Java SE programming as well. Most Java development companies take the help of Spring framework like Spring MVC, Spring Cloud, and Spring Boot to develop a web application and REST APIs.

A good Java developer is also aware of the advantages that the Spring framework offers such as making local Java methods a remote process and also make Java methods execute within a database transaction.

4) Refine your Unit Testing skills

Advanced unit testing skills are to be found in every conditioned Java programmer. This is an essential factor that separates great Java developers from ordinary ones. As a professional Java developer, you should always write unit tests for your code because it helps in the validation of the code at the time of behavior testing or state testing.

Most companies today make sure that as a Java Developer, you have an understanding of the different tools used for automation testing, unit testing, integration testing, and performance testing. 

5) Focus on JVM Internals

Even as a beginner in Java, you are expected to know Java Virtual Machine (JVM), a critical factor of JRE (Java Runtime Environment). Understanding JVM means a much better understanding of Java as a programming language. JVM will assist you in solving complicated issues during the programming process.

As a Java developer, you should also be aware of the JVM restrictions on a stack and the standard errors that a lot of Java developers make.

6) Enhance your knowledge of working around design patterns

The importance of design patterns in software development is surely not hidden from you if you are an object-oriented software developer with some experience. Design pattern helps to depict the relations between the object and classes. If the naming of objects and classes gets done systematically, the recurring issue in these object-oriented systems gets addressed.

Be it a regular employee or even a freelancer, a deep understanding of design patterns is always going to be a big plus.

7) Get acquainted with JVM languages

While learning new languages is always great for you personally and professionally, developing a habit to learn new programming languages apart from Java will help you with the development in Java applications. For instance, Kotlin is a static programming language which operates on Java virtual machines and can be used further for compilation into JavaScript code or LLVM compiler too.

Taking up and learning new languages of programming helps you in making a comparison between the advantages and drawbacks that will, in turn, help you in creating better codes. Help in Android development through it is another plus.

Final Take Away…

If you want to become a pro Java developer and learn new coding and Java programming skills, exploring and showing due diligence to the above tips are bound to take you a long way into the game. Of course, you cannot learn everything all in a single go. Select a particular tip and then proceed towards enhancing it. Be mindful of learning Java 8 as a healthy working experience in Java 8 is essential for developing any application.

The importance of choosing the right technology stack

The use of the right technology stack is the essence of a successful digital product. But choosing the right blend of technology is always tricky. 

At GoodWorkLabs, we offer an expert tech consultation that is unique for every digital product in question. In this post, we have given a more generalized road to help you choose the right tech stack for your application. We are laying down all the possible options for your reference so that you can manifest the right blend for your brand.

Technology stack: Definition & Popular Technology

In layman’s language, web app development requires a database, a server, HTML+CSS, and programming language. All these layers put together, form a tech stack for web development.

Technically, a technology stack is a combination of components which satisfies all the layers of mobile or web application and can directly affect the app functionality. The anatomy here is very simple with two major layers:

• the client-side (frontend; the presentation, what the user sees)
• server-side (backend; the website’s functionality, processes)

Frontend frameworks and libraries:

1) Bootstrap:

• Customizable, saves time, easy to use with a bunch of other helpful components.
• Recommended when you are opting for a ‘mobile first’ application.

2) Angular:

• JS-based framework, good for projects with easy code integration
• New Angular 5 makes it easy to reduce the runtime with the built-in code optimizer
• It is recommended for developing single-page web applications, cross-platform mobile apps, landing pages, and common websites.
• Already used by Google, PayPal, and Upwork

3) Vue.js:

• JS framework which easily integrates with JS libraries
• It is recommended for large-scale and single-page projects.
• Already used by Alibaba, WizzAir, Grammarly

4) React:

• JS library for making user interfaces (UI)
• Active community with numerous ready-made components
• Quick development
• It is recommended for web applications or platforms which require a very responsive UI.
• Already used by Facebook, Reddit, Netflix

5) JQuery:

• JS library that is used for code optimization

Programming Languages:

• PHP:

PHP is particularly designed for web development and creating dynamic web pages. Though it had certain vulnerabilities, it is considered to be the most popular language. Also, as PHP based apps are easy to code, it means that you can cut greatly on expenses by saving time.

• JavaScript (JS):

JavaScript is a convenient, versatile and effective high-level programming language which can be used for both server-side and client-side code. It is recommended for dynamic, agile and modern websites.

• Java:

Java is well-documented and supports numerous libraries. It is used widely for both complex website and dynamic mobile apps. The popular frameworks are Hibernate, Grails, Spring, Dropwizard, and Apache Wicket.

• C#:

With the capability of processing heavy data flow and the flexibility to create all kinds of application, C# is a popular cross-platform technology among developers.

Backend frameworks:

1) Ruby on Rails:

• One of the popular tech stack among startups, ROR is perfect for all kinds of apps from basic web pages to high-traffic web portals. 
• For developers, ROR is very easy to learn and use
• It is fast and scalable
• It uses DRY (“don’t repeat yourself”) design pattern and MVC concept (“model-view-controller”)
• RoR is already used by Airbnb, Basecamp, Twitch, Shopify,  and Zendesk.

2) Django:

• Django is versatile and can be used for startups, medium-sized projects, and high-loaded websites. It is a clean, secure, fast, and scalable framework for rapid development. Along with being well-documented,  it comes with its own lightweight server.
• Already used by Discus, Mozilla, Instagram, and National Geographic.

3) .NET:

• .NET allows developing any type of web app faster and making it scalable. It is very easy to add APIs and live communication features. It has an active community and is extensively documented.
• Already used by Xbox.com, Microsoft, Stack Overflow

4) Node.js:

• Node.js allows optimizing code on complex, high-performance, and data-intensive real-time apps. It is simple, fast, and expressive. It is recommended for apps that involve real-time streaming, collaboration tools, and chatting.

5) Express.js:

• As the name suggests Express.js is a minimalist, flexible, and resource-efficient framework which uses templates and requires minimum efforts. It is recommended for APIs and simple web and mobile services.

6) Flask:

• Flask is another well-documented framework with a highly active community. It is recommended when the client requires to build a service on a resource-constrained system. Also, it is good for serious websites and RESTful APIs.

Databases

1) MongoDB:

• MongoDB is a NoSQL, document-based database which can be used for storing large volumes of unstructured data. It can also be used in a cloud-based environment.

2) PostgreSQL:

• PostgreSQL has multi-version control and supports custom data types. Basically, it is an object-relational database with NoSQL features and is used for storing a gigantic volume of data (up to 32 TB per table).

3) MySql:

• The plus points of this most popular relational database are that MySql is highly scalable, easy to set up, cloud-reafy and is platform independent.

Popular technology stacks

You can also pick from already designed popular web stack. They have a solid foundation and you can easily customize them as per your requirements. The major tech stacks that have been used are LAMP(Linux-Apache-MySql-PHP), MEAN (MongoDB-Express.js-Angular-Node.js) and .NET.

LAMP alternatives:

• WAMP: Windows, Apache, MySql, PHP
• LAPP: Linux, Apache, PostgreSQL, PHP
• WISA: Windows, IIS, SQL, ASP.NET
• XAMPP: Linux, Mac OS X, Windows, Apache, MySql, PHP, Perl
• MAMP: Mac OS X, Apache, MySql, PHP

MEAN alternative:

• MEEN: MongoDB, Ember.js, Express.js, Node.js

Conclusions

The success of your project majorly depends on the tech stack that you choose in the beginning. With so many fishes in the pond, it is difficult to say which one will work best for you. But GoodWorkLabs can help!

Let’s discuss your requirements and compile the perfect tech stack for your next project. Drop us a quick message with your requirements and we will have our tech expert get in touch with you soon

The DevOps Life Cycle – 5 Step Process

DevOps is something that has been garnering a lot of attention whenever software development is brought up as a topic. At GoodWorkLabs, we believe in creating applications that perform consistently well. Applications normally run the risk of crashing or server downtimes thus scaling down the performance of the software.

But before we dive into the technicalities of the DevOps life cycle, let’s first cover the basics.

What is DevOps

The term DevOps is essentially an amalgamation of two words- “development” and “operations.” The entire methodology of DevOps revolves around the process to combine software development with the operations involved in Information Technology. The core goal of DevOps is to ensure a short system delivery life cycle. Once the cycle of a system delivery is brought in check, it becomes easier for any organization to pick up speed and deliver their services and applications to clients quickly.

DevOps is not a term. It is a process that is useful when merged with the culture of an organization and leads to great and consistent results for clients. DevOps helps a company stay afloat in the market. This culture is best suited for large distribution platforms such as websites that involve e-commerce and applications that are stored on a cloud platform.

The DevOps Lifecycle and how GoodWorkLabs deploys reliable DevOps Solutions

Like any consistent process, the DevOps lifecycle also has a lot of phases that comprise its overall identity. To help you understand better, let us discuss a real-time case where the DevOps team at GoodWorkLabs investigated real-time performance issues for online gaming software. The application often crashed when more users got onboard and CPU storage also suddenly spiked to 100% simultaneously thus leading to server downtime. To enhance the performance of this application, the following recommendations were made by the GoodWorkLabs DevOps team:

1. To reduce the number of repetitive calls to dcrypt_card and encrypt_card.
2. Identified particular gameplay that consumed about ~7% of the CPU which led the risk to explode if the number of players for the given gameplay increased.
3. To change the nature of javascript files from synchronous to asynchronous type in order to enhance the performance of the code.
4. Follow an iterative testing model to ensure no bugs enter the game architecture with the changes recommended.
5.  To migrate the database to a NoSQL DB like MongoDB to scale performance. In the long term, this helps to keep performance consistent.

Thus, with the above set of DevOps recommendations, GoodWorkLabs was able to considerably enhance the game experience. To understand how this process works, let’s take a look at the different stages in the DevOps life cycle.

Image reference: DZone

Once the business goals and objectives have been clearly identified and resource planning and optimization is done, the DevOps strategy moves through the following stages.

1. Collaborative Development:

This is the very first phase which involves the planning procedure of your application. Development takes place once you list down a bundle of objectives that are to be achieved through the application you want to create.

Once you close in and take a final call on the objectives, you are ready to head over to carry out the development of your project. This constitutes the procedures of generating the relevant codes and getting them ready for the next phase. Because DevOps is a consistent procedure, existing code can also be used for further enhancements by taking continuous feedback on the development.

2. Integration:

This is a phase that happens as a subsequent step to the development process. Integration comprises of a series of planned steps that need to be executed in the later phases and also analyze as to whether the developed code has a performance that matches up to the set objectives or not. The performance of this code is a big evaluation metric for project documentation.

3. Continuous Testing:

Testing is the phase wherein practical use of the application under DevOps gets merged into. A consumer or a beta tester makes an effort to ensure that the application is made proficient enough to yield plausible results that are feasible in the real world. Testing provides a lot of deeper insights regarding various aspects of the application. This is a great positive as any changes to be made are straightaway sent to the development process again.

Testing enables consistent improvements in the overall working of an application and prepares it for launch in the real world.

4. Continuous Monitoring:

Monitoring is a phase which involves the operational factors of the entire DevOps process. This is where a lot of crucial information about the application usage is saved and intricately processed with the objective of finding out the areas of errors and take note of the present trends that involve the application. This monitoring is more often than not, integrated right within the software that can be in the form of files or even churn out data about certain benchmarks when the application is under use.

5. Continuous Feedback & Optimization

Feedback is the phase that makes consistent improvements within the application. A lot of consistent analysis is done within the operating software to take note of feedback that best describes the current situation of the application. With this regular feedback system, the next development phase of the application is updated with all desired changes.

DevOps has continuity embedded deep into its core, as it is nothing but the redundant steps that elevate an application from a mere project to a sound operating software. This helps the software get better with every update as the development is continuous without any bottlenecks.

Conclusion

DevOps is undoubtedly a great way to undertake the entire procedure of application development. DevOps is what helps a business application to up its game by keeping the feedbacks of the end customer in the forefront throughout the operating life cycle. When it comes to applications that are dynamic and fluid to the trends of the market, it is nothing but DevOps that can help constant evolution and take up new challenges head-on.

If you are looking to optimize the performance of your applications or software and require a proven DevOps strategy, then send us a short message with your requirements.