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.

Share on facebook
Facebook
Share on twitter
Twitter
Share on linkedin
LinkedIn

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.

Share on facebook
Facebook
Share on twitter
Twitter
Share on linkedin
LinkedIn

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.

Share on facebook
Facebook
Share on twitter
Twitter
Share on linkedin
LinkedIn

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 Millions 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 dollars 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

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

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 Scraping

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 scraping 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.

We Use Smart Technologies to Build Smarter AI Solutions

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 deadlines
Integrate your existing platform with highly robust ML model

Case studies