Top business benefits enterprises can achieve by implementing CI/CD pipeline

Top business benefits enterprises can achieve by implementing CI/CD pipeline

In conventional software development processes, releases recur at regular intervals for every small feature update.  The chances of integration at the time of deployment increases in these approaches. Eventually, the problem starts to grow, leaving more challenges for the whole team because of the manual processes. It becomes more prone to human error. CI/CD solves all these issues and makes the entire process more efficient and manageable.

Continuous Integration/Continuous Deployment (CI/CD) is the pillar of developing, testing, and implementing applications to production in present-day software development practices. CI/CD plays a vital role in assisting development and many other teams. CI reduces the risks and allows production consistency by automating multiple code changes from different project developers. And also, CD enables developers to provide the integrated code to production smoothly. It delivers a fast and efficient automated process to release new features and updates to clients without hurdles.

CI/CD has now become an essential part of modern software development approaches. This article analyses the top benefits of the CI/CD pipeline.

Easy Code Integration

One of the technical benefits of continuous integration and continuous delivery is that it enables you to integrate small pieces of code quickly. These code modifications are easier to manage. Also, it contains fewer issues that may require a fix at a later date.

 As soon as the code is pushed into the code repository, the testing starts using continuous testing. This allows developers to understand the problem before the completion of so much work. The communication between the teams gets improvised by this methodology. It is suitable for larger development teams who work remotely and those who work in-house.

Reduced Changes & Review Time

Code modifications done in such an environment minimizes the risk of unexpected consequences.  The changes are easy to manage and fix if any issues pop up.  Once integration takes place, using CI/CD, one can test these code changes rapidly. This methodology is very beneficial when direct communication is not possible or when teams work across remote locations.

Faster Bug Detection and Easier Implementation

Fault isolation defines the practice of designing systems. When an error occurs, the negative results are limited in scope. Limiting the range of issues minimizes the potential for damage and makes systems more comfortable to maintain.

 CI/CD ensures faster fault isolation to detect and easier implementation. Fault isolations comprise monitoring the system, finding when the fault occurred, and triggering its location.  Thereby, it reduces the bugs occurring in the application.

Improves Test Quality

CI/CD enhances the test reliability and quality because of the bite-size and specific changes implemented to the system. It allows teams to perform more accurate positive and negative tests. CI/CD produces continuous reliability through test reliability. 

Rapid Release Rate 

It detects failures quicker and repairs faster, which leads to increased release rates. But, frequent releases are possible only if the code is generated in a continuous operational system.

CI/CD often merges codes, continuously deploys them to production after complete testing, and keeps the code in a release-ready state. It’s crucial to have an environment set up that is used by the end-users. Containerization is one of the best methods to test the code in a production environment. 

Reduces Defects

Incorporating CI/CD into your company’s development process minimizes the number of critical and non-critical defects in your backlog. These small defects are identified before production and fixed before the release.

There are several advantages to solve non-critical problems. For instance, your developers have sufficient time to focus on huge issues and enhancing the system. Testers can concentrate less on small issues so they can identify big problems before the release.

Keeps Your Product Up-To-Date

Focus on the first impressions as they are vital to turning new customers into happy customers. Make your clients happy with new features and bug fixes. Using a CI/CD methodology also keeps your product up-to-date with modern technology and enables you to get new customers with positive reviews.

 Adding new features and modifications into your CI/CD pipeline based on how your customers use the product will allow you to retain existing users and gain new customers.

Continuous Feedbacks

CI/CD is the best way to get continuous feedback from your clients and your team. This improves the transparency of any problems in the team and encourages responsibility. CI concentrates on the development team. This part of the pipeline’s response impacts build failures, design issues, merging problems, etc. CD focuses more on releasing the product quickly to the end-users.

Cost-effective

Automation in the CI/CD pipeline minimizes the number of defects or bugs that occur in CI and CD steps. This reduces the developer’s time and effort and also minimizes the cost. The other benefit is, it increases the code quality with automation and increases your ROI.

Conclusion

Upon knowing the top benefits of implementing a CI/CD pipeline, it is time to make a move. If you are thinking about executing a CI/CD pipeline, you can move forward. It automatically increases the speed and the quality of your releases. These benefits lead to minimized costs and better ROI. Enterprises can spend more time building better products when implementing Selenium test automation with CI/CD.

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Agile Testing Trends to Watch Out in 2021

Agile Testing Trends to Watch Out in 2021

Technology is continually changing. What was ultra-modern a few years back might be obsolete now. Presently, the software development and testing industry is innovating ways to include evolving technologies like artificial intelligence, machine learning, big data, etc.

Be it browser compatibility testing, selenium test automation, or any other kind of testing process, each of these testing processes is consistently transforming with the vision to bring better products. Presently enterprises concentrate more on agility to align better testing methodology based on agile principles.

The contribution of DevOps, Continuous Testing and other factors are expected to elevate agile testing soon. This article analyses some of the crucial agile testing trends expected to impact organizations wishing for quality software testing in 2021.

 AI & ML in Agile Methodologies

The coherent usage of artificial intelligence (AI) and machine learning (ML) in agile methodologies creates an ideal data analysis method. Such collaboration allows software teams to achieve better productivity and efficiency associated with testers and developers. AI & ML together delivers real-time information. Ans also offers a precise prediction of the expected time of the release phase of the project. The inclusion of innovative technologies such as robotics, IoT, quantum computing, etc., is much possible with AI & ML in the software development process.

Sun Technologies’ agile approach with AI & ML provides a good understanding of the best methods for creating testing code. Our experts evaluate code and associated tests to remove bugs. We use innovative technologies for accelerating software development & time-to-market.

Software Quality Engineering 

When you manage testing tasks in an agile environment, the dependency on quality engineering is more. What is the dissimilarity between quality engineering and quality assurance? Quality engineering deploys continuous testing of the related product with the extensive implication of automation. It ensures that the product is placed under efficient testing to make it error-free.

Benefits:

  • Provides faster feedback on the software product because continuous testing is done across various platforms and operating systems
  • Minimizes software failures and availability of early feedback

Shift-Left Testing Approach

The shift-left process emphasizes various types of testing performed simultaneously with software development— testers collaborate with developers to frame test cases. The method also includes what-if scenarios, and the tests are used for streamlined development.

In the usual software development process, the incorporation of testing is seen as a blockage to the release process. Testers operate on less time due to tight schedules, thereby hindering testing efforts and identify errors. However, by the shift-left process, testers get a sufficient amount of time to test the software’s usability by comfortably teaming up with UI & API developers.

Benefits:

  • Faster time-to-market for early release
  • More rapid identification of bottlenecks to avoid software failures
  • Provides high performing software under minimal time

Agile Test Management

For any software testing services, the collaboration of testers and developers is a must. Such cooperation includes the structure, execution, and report of testing once the results are out.

The agile test management’s involvement helps determine the processes and tools that allow the overall team to maintain testing progress. The process brings together everyone on the same path. For experts working in distributed environments, a cloud-based test management tool’s availability confirms easy access to testing information anytime and anywhere.

Benefits:

  • Allows team members to trace testing efforts and increase collaboration
  • Minimizes the bugs and accelerates the release of high-quality software
  • Ensures real-time feedback

DevOps Process 

The concept of DevOps is based on lean management. It concentrates on combining development and operations to create a suitable environment. A DevOps approach refines the software development lifecycle and eliminates junk, and accelerates software delivery. The combination of Agile and DevOps can fine-tune team relationships and communication, thereby minimizing software failures. Moreover, DevOps skillfully combines continuous testing into the development process to ensure code quality.

Benefits:

  • Set up a proper collaborative culture
  • Integrates development and testing processes 
  • Combines operations within the team to reduce downstream testing concerns

Continuous Testing 

The process of Continuous Testing includes redundant executing tests that deploy testers into cross-functional teams. This arrangement helps refine testing functionalities and offers rapid feedback. Continuous testing supports early testing along with shift-left, agile test management, and quality engineering.

Benefits:

  • Reduces software failure by early detection
  • Enhances software quality through ongoing reviews and reports
  • Improve test suites to identify business risks

Lean Portfolio Management 

Lean portfolio management follows a different methodology that focuses on streamlining operations to measure outcomes based on organizational goals and planning. It mainly follows a continuous process used to assign tasks within teams based on the priority and well-organized strategy.

As per Lean portfolio management, the paramount importance is on the collaboration running from top to bottom, covering factors connected with goal measurement, planning, and work transparency.

 Benefits:

  • Enhances the relationship between organizational strategy and individual projects
  • Fine-tune business value to get clarity about the software 

Businesses cannot ignore the impact of agile testing in today’s progressive testing environment. Therefore, technological upgrades to enhance agile testing is the main motto for most software companies. At Sun Technologies, we deliver world class QA services. Get in touch with one of our solution consultants today to understand your business requirements and find suitable solutions. Discover more about the various attributes of agile testing with us

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How Artificial Intelligence and Machine Learning can optimize DevOps?

How Artificial Intelligence and Machine Learning can optimize DevOps?

DevOps boosted by AI/ML

Seven out of ten customers use DevOps and IT Services. Vendors are under tremendous pressure to fulfill clients’ evolving needs to create a self-healing system and increase automation. Sun Technologies uses AI/ML solutions to improve the efficiency of the DevOps Pipeline. With our tried-and-tested AI Solution, we help our customers create a self-healing system, reduce time-to-market, and improve efficiency using DevOps powering it with AI/ML solutions. 

In a few cases, it may be hard for some organizations to use AI and ML due to the complexities. Adapting to AI/ML solutions within DevOps is a cultural shift. 

With Machine Learning, several models can be created to analyze the DevOps metrics that include:

Our trained models help customers in: 

  • Analyze metrics
  • A deep-dive analysis of repeated failure
  • Executing automation sets
  • Predicting failure points before the occurrence

Based on the result-sets aggregated from the model, AI helps make automated prediction-based decisions to avoid failures.

Recent research states that around 85% of C-level officials trust the AI/ML can offer considerable value concerning accuracy and decision-making, prompting improved organizational productivity.

AI/ML on each phase of DevOps

There is a wide misconception across the industry in understanding DevOps. Automation is not the entire DevOps world, but just a yield. DevOps is a cultural shift for Developers, Business Users, Infrastructure engineers, and a few other key stakeholders. 

DevOps has various features that include Continuous Integration, Continues Delivery, Continuous Monitoring, Continuous Testing, and Continuous Security. AI/ML has its role in each feature sets. 

Sun Technologies helps customers build a cloud-agnostic and cloud-native DevOps pipeline and improve efficiency throughput using AI/ML solutions. We bring prediction, Learning, and Automation together. 

Few use-cases AI/ML within DevOps pipeline implemented by Sun Technologies includes: 

  • Automated Code rollback for wrong check-ins 
  • Automated Log analyzer to identify security threats such as intrusion, DDoS, DoS
  • Self-Healing Web-Application System
  • Alerting mechanism for potential (Futuristic Failures) 
  • Chatbot / Voice Automated Deploy Assistant
  • DevOps Advisor suggesting automation of repeatable tasks

DevOps optimization using AI/ML

  • The three well-defined capabilities AI brings are prediction, self-learning, and automation
  • AI and ML dispatch data with self-learning capabilities, making AI and ML techniques exceptionally advantageous if imbibed into the DevOps Pipelines
  • During SDLC, AI/ML can monitor and track production performance to which the end-user experience is being labeled by simulating different possible scenarios
  • With AI/ML incorporated into the DevOps process, the DevOps teams can know how the code is performing
  • AI allows managing the growing volumes of data in DevOps environments
  • With Chatbot/Voice assisted DevOps, Developers can check-in the code and make deployments with a single command 

Tangible benefits

  • Faster Time to Market using DevOps while AI/ML boosting it further to make it more efficient
  • Proactive decision making than reactive
  • Satisfied Business users
  • Low/No human intervention
  • Realistic Instant RoI
  • Adaptable and Maintainable DevOps pipeline
  • Huge Time Savings
  • Increased Efficiency

Conclusion

Enterprises can apply AI and ML to enhance their DevOps condition. AI can help predict complex data pipelines and make models that can enhance the application development process. However, implementing AI and ML for DevOps likewise exhibits various difficulties for enterprises.

Organizations envisioning DevOps have to set up a well-defined DevOps roadmap before full-fledged implementation. When the establishment is made, AI/ML should be viewed only as a booster to increase efficiency and effectiveness. AI/ML helps DevOps teams to concentrate on inventiveness and innovation by taking out negative aspects over the operational life cycle. It brings about automated improvement and expansion in the DevOps team’s effectiveness.

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Artificial Intelligence and the keys for its success

Artificial Intelligence and the keys for its Success

Artificial intelligence has transformed the world as we knew it. Associations and enterprises of all industries worldwide are already using AI in multiple ways to support their business operations. However, regardless of the expanded appropriation of AI, ongoing research from International Data Corporation(IDC) has discovered that half of AI projects fail for one out of four organizations. It has been found that the two primary causes for these flops are an absence of required aptitudes and unrealistic expectations. When utilized properly, big data and AI can help C-suite professionals with a wide array of functionalities. If executed right, your capacity for its’ implementation and uses are constrained only by your ability to imagine how these innovations can profit us. With the correct arrangement of standards, in any case, organizations can make AI frameworks that successfully scale, with the least expense.

 

Our observations uncover the difficulties and successes that organizations have had with AI and how enterprises have utilized what they’ve figured out to drive future AI achievement. This report centers around the key jobs and the significant part they play in making AI progress. Below is a list of key plan standards for AI solutions, in light of exercises gained from the research of numerous successful AI executions.

AI in Data Analytics

From a business standpoint, data analytics can build an income with evolving patterns and improve operational productivity. With such many trendy expressions such as data repositories, machine learning, and artificial intelligence, it can be not easy to recognize where the value of data analytics is coming from and what an outside supplier can offer. One of the most troublesome aspects seen by organizations in data analytics is that some types of information sources can be incredibly hard to analyze. As information sources are different and divided frequently, manual information has been a prerequisite for analyzing the data. Studies show that this procedure of information readiness takes around 80% of the usual time. 

Likewise, a significant part of the data created by organizations has zero conventional structure; agreements, overviews, and emails all hold a plethora of information that analysts can use to reveal openings. In their day-to-day activities, people use the sharing of data in almost every aspect of their lives. Just through the simple act of a Google search alone, an abundance of data can be accumulated. This data creates large volumes of information that is consistently gathered and being consumed with the intent of, for the most part, improving our lifestyle

AI in Machine Learning & Data Management

In the present world, business ventures are seeing an explosion of data like never before. From more traditional information gathering sources such as data libraries, networking areas to more current ones that include the Internet of Things (IoT) sensors and associated gadgets, we see an un-capped potential for information gathering. Further, the advancement of cloud innovations has framed.

 

As artificial intelligence and machine learning innovations continue developing, business appropriation of data management will begin to happen more rapidly within the worldwide business scene. The main goal of data management solutions via artificial intelligence is to bring partially pre-defined arrangements at a reasonable cost to medium and small entrepreneurs’ hands so that these innovations have the vastest reach. 

AI & Compute

Algorithmic innovation, data, and the quantity of computing available are the three driving factors in artificial intelligence advancement. The use of large computes can, at times, deliver negative points of present algorithms. Still, more process appears to lead typically to do better execution and is frequently corresponding to algorithmic advances.

Counting Operations in the model and the GPU time are two methodologies used to generate data points. The operations can be computed manually or programmatically. Artificial intelligence turns into the focal point of computing, pushing at the limits of what systems can do, somewhat in the “deduction task,” where neural nets make forecasts, yet considerably more so for preparing a neural net, the more process concentrated capacity. Reinforcement learning will keep advancing, but, to genuinely arrive at human or more prominent level knowledge, unusual perspective changes may need to happen. These techniques may revolve around quantum mechanical and computing frameworks because the ideas of quantum computing consider the probabilistic notion of artificial intelligence. The too high dimensional systems are allowed by the quantum rather than a single input at a time.

Three Important Key Fixings

Most importantly, to figure out where an issue happens, you have to have a clear and total perspective on your whole environment. That implies you have to correlate information from business exchanges (like requests and invoices) through application services (like web servers and databases) and infrastructure (like registers and systems). Second, you have to have AI analytics that can decide designs over the whole stack. Ultimately, you should have the option to visualize future occasions. Without a multi-space, multi-layer, and multi-seller relationship, IT groups should pass through several monitoring devices and logs and burn certain times to comprehend what identifies with what.

The key players and the significant part they play in making AI Success:

This report lists the leading players of artificial intelligence and focuses on the significant part they play in making AI successful.

Role of Executives in AI

Executives need to recognize how AI can carry their business to adequately lead the charge and set the remaining organization’s pace. If an organization’s executives don’t understand the importance of AI, that lack of understanding can translate to detrimental delay in bringing innovation into their enterprise. Chief Digital Officers, Chief Analytics Officers, etc., have been established with that in mind, giving organizations trained professionals that have specialized knowledge and can assist the other internal executives in those selections. 

Even within organizations that have internal SMEs to help with this process can run into barriers to success. Worldwide organizations with various divisions spread out across a wide array of business units located in geographically diverse locations that may face difficulties with getting everybody in a single pipeline. Huge universal enterprises are complicated and hard to explore. Strong executive sponsorship is the best way to get through this barrier.

Role of Early Adopters in AI

Organizations that put resources into developing AI and adopt it early will benefit in the long run. Indeed, the individuals who are slower to embrace or don’t receive by any means will fall behind. A lot of organizations lack this by concentrating only on AI in a single business process. Applying AI to only a single use case is not a good idea. The commitment to the AI adoption of the entire business is the key to a successful early adopter.

Role of Team Players in AI

The adoption of artificial intelligence can change each business territory, extending from marketing endeavors to the client experience. In this present reality, where data scientists don’t meet the need for AI solutions, each player counts and can have any effect. To assemble all the AI that an organization needs, everybody must contribute.

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AI and Digital Transformation: 4 Ways to Greater Automation

AI and Digital Transformation: 4 Ways to Greater Automation

The initial phase of digital transformation is still an ongoing process for many enterprises, centering around the digitalization of products, services, and business processes. The second phase uses AI to enhance the quality of decision-making, strengthen the relationships with customers, and optimize organizational productivity. 

While various enterprises are at various stages of maturity of digital transformation, many companies have already been experimenting with AI separately to discover how it could benefit the business in the second phase of digital transformation.

One of the crucial reasons for the second phase of digital transformation’s unsymmetrical results is the understanding or lack of knowledge of what Artificial Intelligence can do. There has been a general illusion that artificial general intelligence (AGI) can solve any issue when truly artificial narrow intelligence (ANI) is the ultra-modern.

AI in Digital Transformation

The digital transformation journey has crossed from digitization to digitalization. It also covers different data technologies across various industry verticals. To the extent that data-driven innovations are helping to manifest several advances in digital technologies as actionable, the next boundary in re-molding enterprises is AI transformation. Progression in AI is forcing digital enterprises toward becoming intelligent enterprises. AI technologies are already revolutionizing not just how we recognize and do business, but also the business environment and its’ overall landscape.

1.Augmented Analytics

Business intelligence is going far beyond dashboards. AI and machine learning are becoming a much more user-friendly process for inexperienced workers as augmented analytics are embedded into platforms.

Organizations are struggling to get their data management and machine learning practices up. This is where augmented analytics are arriving at the rescue. What’s more, it could also help with utilizing machine learning for production purposes, which has been a problem for many enterprises.

The benefits of this have the potential to extend far beyond business intelligence. The passion for implementing AI in enterprises was high at the initial stage. By adding automation to parts of business operations, such as data pipelines and data management, augmented analytics can be one of the elements of the solution to acquiring AI into enterprise production.

2.Automation

Automation has moved past the workforce to white-collar tasks that are repetitive. Just like with some robotic process automation (RPA) tools and chatbots, several automated systems are not necessarily “intelligent” because they are inevitably programmed. i.e., a given input produces a given output. AI enlarges the scope of what automated systems can do and moves enterprises to the second phase of digital transformation.

3.Customer Engagement and Resources

More than half of organizations acknowledge not having a formal customer engagement program established. Because of this, those organizations had no grasp on the number of customers they’d actually lost in a one year period. On the other hand, most customers expect a consistent experience wherever they engage. Digital transformation using AI can help optimize customer engagement by dynamically aligning the website content with the customer’s preferences.

4.AI-Digitized supply chains

AI solutions and tools help analyze large datasets in real-time, plan production efficiently, balance supply and demand gaps, schedule factory activities effectively, and develop error-free SCM plans and strategy. AI can also help estimate the market requirement and manage production accordingly to avoid overproduction or shortage of products, either of which would result in loss.

Conclusion

The future of connected digital transformation includes more IoT and industrial IoT devices and the coordination of AI.

IoT and IoT devices are already offering businesses the clarity at the edge, which they lacked before. When combined with AI, the sensors are helping to change the ways companies operate to yield optimization.

Enterprises with and without AI-enabled digital transformation models may adopt some AI by default because it has been embedded in the software tools, applications, and platforms they already utilize. Data scientists should aim to solve complex issues and bring more value from AI in the second phase of digital transformation. Simultaneously, citizen developers (power users) can tackle easier problems, such as optimizing business processes and tasks within their department.

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Automation testing for a Leading Finance/Insurance company operating across USA

Case Study

Automation testing for a Leading Finance/Insurance company operating across USA

Whom we worked with

A top-tier insurance company that offers financial products and services including life insurance, annuities, mutual funds, disability income insurance, credit union products, retirement planning, and more.

Our Solution

  • Developed a framework to integrate with Test Management tool ALM
  • Created a completely new documented business process
  • Designed test plan and roadmap with milestones and estimates

Challenges

  • Lack of dedicated QA team with the client to support the automation script development
  • Integration of Selenium Framework with ALM/QC
  • Automating mainframe applications

Impact

  • Replaced 90% of manual validation with Test Automation
  • With one button click, they are able to validate applications after monthly patches deployed
  • After Test execution test results were reported in HTML files and these reports are emailed to all asset owners automatically
  • Reduced 70% of maintenance effort for Test automation scripts and provided 100% customizable test report
  • Saved Milions of Dollars in Testing costs by eliminating all manual effort

How we helped

  • Sun Technologies designed a high-level test plan which provided visibility on effort estimation, deliverables, and test approach
  • We developed a Robust Automation framework using Selenium which would allow triggering test execution from ALM which reduced 80% of manual effort
  • Automated the complex scenarios of 20 Mainframe applications
  • Accomplished an optimal level of automation for their 300+ applications in Production and Test environments
  • Test Automation replaced 90% of manual validation during monthly patch releases this helped the client in saving millions of dolars of testing costs by eliminating manual efforts.
  • Test automation scripts are used in daily batch executions to ensure all the 300+ applications are up and running
  • We have taken additional responsibilities with Release Management to assist the process to become more robust

AI & ML

Artificial Intelligence & Machine Learning

Providing you with exceptional AI & ML services for your needs

Sun Technologies’ Artificial Intelligence and Machine Learning Center of Excellence augments our client’s AI teams involved in solving complex business problems and enabling them to move faster and deliver better results overall.

Our service offering includes

  • Machine Learning
  • Deep Learning
  • Chatbot Development
  • Data Science
  • Robotic Process Automation
  • Data Scrapping

Machine Learning

Sun Technologies leverages the full potential of pattern recognition, mathematical optimization, computational learning theory, self-optimizing, and nature-inspired algorithms to the fullest advantage of its customers, providing tailor-made machine learning services and solutions

Deep Learning

Sun Technologies’ custom-built deep learning solutions help you build powerful and intelligent artificial neural network models that are efficient enough to automatically learn complex representations of data. We have full-stack deep learning, AI, machine learning experts and data scientists who have a deep understanding of a variety of deep learning techniques and the best practices used in deep learning use cases for multiple industries. Speech Recognition, Transcribe and transform human speech into format useful for computer applications.

Chatbot Development

We develop intelligent AI applications and systems that can be trained to interact with humans using touch sensing, voice recognition, language and intent recognition, and programmed decision making.  We have expertise in state of the art AI technologies like Caffe, DeepLearning4J, Google AI, Tensor Flow, Theano, Torch, etc.  Bots are text-based programs powered by artificial intelligence and natural learning processes that interact with users over a variety of platforms.

Data Science

Our data science as a service helps enterprises make informed data-driven business decisions and find innovative ways to strategize and optimize operations, while discovering new market opportunities and gaining the benefits.

Robotic Process Automation

Sun Technologies works with organizations to build automated solutions that help them navigate data touch points that are unstructured in nature and then helping them mature towards intelligent systems that make optimal decisions while learning continuously from their environment.

Data Scrapping

We have a team of skilled and seasoned data scraping experts who are well versed with the latest data scraping tools and methodologies. Sun Technologies offers quality data scrapping services to businesses around the world. Our utilization of cutting-edge tools ensures that we can even mine data from sites using JavaScript, jQuery, MooTools framework, etc., and CAPTCHA challenge response systems enabled sites.

Sun Technologies Data Science Advantages

  • Data expert who have the technical skills to solve complex business problems
  • Get real-time insights into your business performance and avoid any forthcoming risks
  • Personalized (Do you mean Customized?) solution for your model that promotes automation of your processes
  • Complete transparency of the work and processes during the development of the product
  • Agile Methodology is our forte and we are expert at meeting our time deadlines
  • Integrate your existing platform with highly robust ML model

We Use Smart Technologies to Build Smarter AI Solutions

Case studies

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