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4 AI and Analytics trends to watch for in 2020-2021

Never did we imagine the fictional robotic characters in novellas to become a reality. However, we wished, didn’t we? The theory of ‘Bots equal to Brains’ is now becoming a possibility. The mesmerizing and reverence Artificial Intelligence (AI) that we as children saw in the famous TV show- The Richie Rich has now become a plausible reality. Maybe, we are not fully prepared to leverage AI/Robotics as part of our daily lives; however, it has already created a buzz, profoundly among the technology companies. AI has found a strong foothold in the realms of data analytics and data insights. Companies have started to leverage advanced algorithms garnering actionable insights form a vast set of data for smart customer interactions, better engagement rates, and newer revenue streams. Today, Intelligence-driven Machine Learning intrigues most companies in different industries globally; however, not all exploit its true potentials. Combining AI with Analytics can help us drive intelligent automation delivering enriched customer experiences. Defining AI in Data Analytics This can be broad. However, to summarize, it means using AI in gathering, sorting, analyzing a large chunk of unstructured data, and generating valuable and actionable insights driving quality leads. Big players triggering the storm around AI AI may sound scary or fascinating in the popular imagination; however, some of the global companies have understood its path-breaking impact and invested in it to deliver smart outputs. Many big guns like IBM, Google, and Facebook are at the forefront, driving the AI bandwagon for better human and machine co-ordination. Facebook, for instance, implements advanced algorithms triggering automatic photo tagging options and relevant story suggestions (based on user search, likes, comments, etc.). However, with big players triggering the storm around AI, marketers are slowly realizing the importance of humongous data available online for brand building and acquiring new customers. Hence, we can expect a profound shift towards AI application in Data Analytics in the future. What’s in store for Independent Software Vendors (ISVs) and Enterprise teams With the use of machine learning algorithms, Independent Software Vendors and Enterprise teams can personalize the product offerings using sentimental analysis, voice recognition, or engagement patterns. The application of AI can automate the tasks while giving a fair idea of their expectations and needs. This could help product teams in bringing out innovative ideas. Product specialists can also differentiate between bots and people, prioritize responses based on customers, and identify competitor strategies concerning customer engagements. One of the key elements that AI will gain weight among product marketers will be its advantage in real-time response. The changing business dynamics and customer preferences make it crucial to draft responses in real-time and consolidate customer trust. Leveraging AI will ensure that you, as a brand, are ready to meet customer needs without wasting any time. Let us understand a classic example of how real-time intelligent social media analytics can create new opportunities. Lets read about 4 AI and Analytics trends to watch for in 2020-2021 1. Conversational UI Conversational UI is a step ahead from pre-fed and templated chatbots. Here, you actually make a UI that talks to users with human language. It allows users to tell a computer what it needs. Within conversational UI, there is written communication where you would type in a chatbox and voice assistant that facilitate oral communication. We could see more focus on voice assistants in the future. For example, we are already experiencing a significant improvement in the “social” skills of Crotona, Siri, and OK Google. 2. 3D Intelligent Graphs With the help of data visualization, insights are presented interactively to the users. It helps create logical graphs consisting of key data points. It provides an easy to use dashboard where data can be viewed to reach to the conclusion. It helps quickly grasp the overall pattern, understand the trend, and strike out elements that require attention. Such interactive, 3D graphs are increasingly used by online learning institutes to make learning interactive and fun. You will also see 3D graphs used by data scientists to formulate advanced algorithms. 3. Text Mining It is a form of Natural Language Processing that used AI to study phrases or text and detect underlying value. It helps organizations to segregate information from emails, social media posts, product feedbacks, and others. Businesses can leverage text mining to extract keywords, important topic names, or highlight the sentiment – positive, neutral, or negative. 4. Video and Audio Analytics This will become a new normal in the coming few years. Video Analytics is computer-supported facial recognition, gesture recognition used to get relevant and sensitive information from video and audios to reduce human efforts and enhance security. You can use it in parking assistance, traffic management, access authorization, among others. Can AI get CREEPY? There is a growing concern over breach of privacy by the unethical use of AI. Are the concerns far-fetched? Guess not! It is a known fact that some companies use advanced algorithms to track your details such as phone numbers, anniversaries, addresses, etc. However, some do not limit to the aforementioned data, foraying into our web-history, traveling details, shopping patterns, etc. Imagine your recent picture on Twitter or Facebook, which has a privacy setting activated used by a company to create your bio. This is undoubtedly creepy! Data teams should chalk down key parameters to acquire data and share information with the customers. Even if you have access to individual customer information like their current whereabouts, a favorite restaurant, or favorite team, one should refrain from using it while interacting with customers. It is your wisdom to diligently using customer data without intruding on their privacy. Inference Clearly, the importance of analytics and the use of AI for adding value to the process of data analysis is going up through 2020. With data operating in silos, most organizations are finding it difficult to manage, govern, and extract value out of their unstructured data. This will make them lose on a competitive edge. Therefore, we would experience a rise of data as a service that will instigate the onboarding of specialized data-oriented skills, finely grained business processes, and data-critical functions.

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AI artificial intelligence Big Data Analytics

5 Top AI Challenges in Cybersecurity You shouldn’t Overlook

Advancement in technologies has created umpteen opportunities for cybercriminals to steal data. The rise in the use of cloud technology has accelerated the process of sharing of data online – information is now available irrespective of place and time. The odds are far more favorable than before for cybercriminals to get into your system. Organizations are firefighting cyber threats at two fronts – from amateur script artists who consider hacking more as awarding than rewarding, and attacks backed by organized crime syndicate with intentions to de-stabilize operations and damage the economy. Per a report by Security Intelligence, the average cost of a data breach is $3.92 million as of 2019. Cybersecurity Ventures predicts that the damage to the world due to cybercrime will reach $6 trillion annually by 2021. This represents the greatest transfer of economic wealth in history, risks the incentives for innovation and investment, and will be more profitable than the global trade of all major illegal drugs combined. This amount will only climb up until we do away with the firefighting approach and think more proactively, It takes a thief to catch a thief To beat some in their own game, you must think like them. If they are fast, you must be fast; if they are cutting-edge, you must be cutting edge. To counter the threats posed by cybercriminals, organizations ought to be faster. It requires to do away with traditional security measures and embrace new age, automation-driven practices that could put us ahead of any hacker. The regular practice includes securing only mission-critical parts within an infrastructure. This leaves room open for hackers to target non-critical components. Therefore, organizations must implement comprehensive and robust cybersecurity procedures that cover every component within an infrastructure. Further, organizations should align themselves with the practice of leveraging automated scripts to facilitate continuous monitoring and reporting in real-time. Ushering an era of proactive cybersecurity via Machine Learning Artificial intelligence (AI) and machine learning (ML) gives an edge to modern software that are primarily created to protect from unethical cyber practices. With AI and ML, the cybersecurity software products get an extra sense to underline concurrent behavioral patterns of the workflows, assess its threat level and, based on it, alert the concerned team. The key reason why AI/Ml can perform such activity is its ability to gauge data, compare it with past actions, and derive an inference. This inference provides the security team an insight into future events that could lead to a possible cyber-attack. However, AI application is still in the nascent stages. Per IDC, one in four AI project usually ends up failing. This means there are challenges we must counter in order to make AI a success. These challenges become significant when the matter is about the organization’s data security. Let us now analyze 5 top challenges that prevent the successful implementation of AI/Ml for cybersecurity. 1. Non-aligned internal processes Most companies have optimized their infrastructure, especially its security components, by investing in tools and platforms. Yet, we see that they face security hurdles and fail to safeguard themselves against an external attack. This is a result of a lack of internal process improvements and cultural change that prevents capitalizing the investments in security operation centers. Further, the lack of automation and fragmented processes creates a less robust playground to defense against cybercriminals. 2. Decoupling of storage systems Most organizations do not leverage data broker tools like RabbitQ and Kafka to initiate analytics of the data outside the system. They do not decouple storage systems and compute layers, which doesn’t allow AI scripts to execute effectively. Further, a lack of decoupling of storage systems increases the possibilities of vendor lock-ins in case of a change in the product or platform. 3. The issue of malware signature Signatures are like fingerprints of malicious code that assist security teams in finding the malware and raising an alert. The signatures do not match the growing number of malware every year. The concern is that any change in the script of the virus makes the signature invalid. In short, signatures will only help debug malware if the code is pre-established by security teams. 4. The increasing complexity of data encryption The rise in the use of sophisticated and advanced data encryption strategies are making it difficult to isolate an underlying threat. The most common way to monitor external traffic is via deep packet inspection (DPI) that helps filter external packets. However, these packets consist of a predefined code characteristic that can be weaponized to infiltrate in the system by the hackers. Further, the complex nature of DPI puts pressure on the firewall, slowing down the infrastructure speed. 5. Choosing the right AI use cases More than 50 percent of the AI implementation project fails in the first go. This is because organizations try to adopt AI on a company-wide level. They often neglect the importance of baby steps – narrowing down on AI-based use cases. Thus, they miss out on initial learning curves and fail to absorb critical hiccups that often jeopardize the AI projects. AI/ML isn’t a magic bullet rather AI/Ml isn’t a cure-all to the activities of cybercriminals. Rather, a fierce defense that is rooted in intelligence and intuition. AI/ML will help create intelligent systems that work as a potent defensive force against activities. They could detect and alter, but they can’t reason why and how these activities were triggered. It is the security teams that need to carry out root-cause analysis of the incident/s and then remediate it. Take Away Mature processes, cultural alignment, and skillful teams and choosing the right AI use cases in cybersecurity are the key to the success. For this, security teams must carry out an internal audit and tick mark areas in infrastructure that are the most vulnerable. Ideally, they can start with data filtering to segregate unauthenticated sources. This isn’t the thumb rules, though. The bottom line is taking mindful steps towards adopting AI for cybersecurity.

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AI artificial intelligence cyber security

7 Ways AI Speeds Up Software Development in DevOps

I am sure we all know that the need for speed in the world of IT is rising every day. The software development process that used to take much longer in the early stages is now being executed in weeks by collaborating distributed teams using DevOps methodologies. However, checking and managing DevOps environments involves an extreme level of complexity. The importance of data in todays’ deployed and dynamic app environments has made it tough for DevOps teams to absorb and execute data efficiently for identifying and fixing client issues. This is exactly where Artificial Intelligence and Machine Learning comes into the picture to rescue DevOps. AI plays a crucial role in increasing the efficiency of DevOps, where it can improve functionality by enabling fast building and operation cycles and offering an impeccable client experience on these features. Also, by using AI, DevOps teams can now examine, code, launch, and check software more efficiently. Furthermore, Artificial Intelligence can boost automation, address and fix issues quickly, and boost cooperation between teams. Here are a few ways AI can take DevOps to the next level. 1. Added efficiency of Software Testing The main point where DevOps benefits from AI is that it enhances the software development process and streamlines testing. Functional testing, regression testing, and user acceptance testing create a vast amount of data. And AI-driven test automation tools help identify poor coding practices responsible for frequent errors by reading the pattern in the data acquired by delivering the output. So, this type of data can be utilized to improve productivity. 2. Real-time Alerts Having a well-built alert system allows DevOps teams to address defects immediately. Prompt alerts enable speedy responses. However, at times, multiple alerts with the same severity level make it difficult for tech teams to react. AI and ML help a DevOps team to prioritize responses depending on the past behavior, the source of the alerts, and the depth. And can also recommend a prospective solution and help resolve the issue quicker. 3. Better Security Today, DDoS (Distributed Denial of Service) is very popular and continuously targets organizations and small and big websites. AI and ML can be used to address and deal with these threats. An algorithm can be utilized for differentiating normal and abnormal conditions and take actions accordingly. Developers can now make use of AI to improve DevSecOps and boost security. It consists of a centrally logging architecture for addressing threats and anomalies. 4. Enhanced Traceability AI enables DevOps teams to interact more efficiently with each other, particularly across long distances. AI-driven insights can help understand how specifications and shared criteria represent unique client requirements, localization, and performance benchmarks. 5. Failure Prediction Failure in a particular tool or any in area of DevOps can slow down the process and reduce the speed of the cycles. AI can read through the patterns and anticipate the symptoms of a failure, especially when a pre-happened issue creates definite readings. At the same time, the ML models can help predict an error depending on the data. AI can also see signs that we humans can’t notice. Therefore, these early notifications help the teams address and resolve the issues before impacting the SDLC (Software Development Life Cycle). 6. Even Faster Root Cause Analysis To find the actual cause of a failure, AI makes use of the patterns between the cause and activity to discover the root cause behind the particular failure. Engineers are often too preoccupied with the urgency to going Live and don’t investigate the failures thoroughly. Though they study and resolve issues superficially, they mostly avoid detailed root cause analysis. In such cases, the root cause of the issue remains unknown. Therefore, it is essential to conduct the root cause analysis to fix a problem permanently. And AI plays a crucial role here in these types of cases. 7. Efficient Requirements Management DevOps teams make use of AI and ML tools to streamline each phase of requirements management. Phases such as creating, editing, testing, and managing requirements documents can be streamlined with the help of AI. The AI-based tools identify the issues covering unfinished requirements to escape clauses, enhancing the quality and the accuracy of requirements. Wrapping Up Today, AI speeds up all phases of DevOps software development cycles by anticipating what developers need before even requesting for it. AI improves software quality by giving value to specific areas in DevOps, such as improved software quality with automated testing, automatically recommending code sections, and organizing requirement handling. However, AI must be implemented in a controlled manner to make sure that they become the backbone of the DevOps system and does not act as rogue elements that require continuous remediation.

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AI devops DevOps testing

Artificial Intelligence – the fuel for digital growth

Driving Digital Transformation with AI Artificial Intelligence has become the fuel of digital disruption. The real-life benefits for a few initial adopters have already started yielding results. For others it has become more important to begin their digital transformation without further delay. AI technology systems like computer vision, robotics and autonomous vehicles, natural language understanding, virtual advisors, and self learning machines that use deep learning and support many recent advances in AI, have become mainstream. As industries and businesses struggle to yield the benefits of AI, they are realizing that it is easier said than done. A good company that can render profound Artificial Intelligence Services is what most businesses need, so that they can continue to focus on the development and marketing of their products. The Roller Coaster Ride The idea of Artificial Intelligence started gaining impetus post the development of computing. It has also experienced its wave of glory and dismay. One thing AI was yet to experience was the large scale commercial deployment, but that is slowly changing too. Machines powered by Deep Learning, a subset of AI, can perform multiple activities that require human cognition. This includes understanding complex patterns, curating information, reaching conclusions and even giving out predictions with suggested prescriptions. The capabilities of AI have significantly broadened, so has its usefulness in many fields. Although one key thing we should not forget is that machines do have some limitations. To take a relevant example, machines are always susceptible to bias as they depend on training data and are trained on specific data sets. Comprehensive dataset is still a relative term. It is both driven by available data and the modellers understanding of use case. Although, irrespective of all these limitations we are experiencing commendable progress. Driving out of the dreaded ‘AI Winter’ of 1980’s, AI powered by machine learning has scaled up since 2000 and has driven deep learning algorithms. The key things that have facilitated these advances are Availability of huge and varied datasets that are comprehensive in nature Improved models and modelling techniques that can self learn using reinforcement Increase in R&D funding Powerful computing hardware and processing units such as GPU, NPU etc. that are 80 – 90 times faster than normal Integrated Circuits The Promise – Boosting Profit and Driving Transformation Adoption of AI still remains in its very initial days. Thus it still remains a big challenge to assess the real potential impact of AI on various sectors. Early evidence suggests that if AI is implemented at scale it does deliver good returns. AI can even transform business activities. It can reshape functions across the value chain and the cases can have major implications for many stakeholders, ranging from MNC, SMB, Government, and even social organizations. “Extensive financial growth will be seen by those organizations, which will combine a proactive AI strategy with its strong digital capability.” Some of the digital native companies have made early investments in AI and they have even yielded a potential return on investment. A case in point can be Netflix that uses algorithms to personalize recommendations to its worldwide subscribers. Customers tend to have a patience span of only 90 seconds and give up if they are not able to find their desirable content within this time. Netflix satisfies this discovery through better search results. This has helped it to avoid cancelled subscriptions that otherwise would have reduced its revenue annually by $1 billion. The expectation that has been set on AI will need it to deliver economic applications that can significantly reduce costs, enhance utilization of assets and increase the revenue. AI can help create value in following avenues: Enable organizations to better budget and forecast demands, Optimize research and better sourcing; Enhance ability to produce goods and deliver services at lesser cost but higher quality; Help tag the right price to offering, with an appropriate message, and targeted to the right customers; Provide personalized and convenient user experience The listed points are not exhaustive but are based on the current knowledge of applied AI. AI will also have unique degrees of relevance for each industry, the prospect and application levers are particularly rich with troves of opportunities. Machine Learning powered by deep learning can bring deeper and long term value to all sectors, few technologies are exceptionally suited for business applicability. Some specific use cases are cognitive robots for retail and manufacturing, deep machine vision for health care, and natural language understanding and content generation for education. Industries disrupted by AI Financial Services AI has significantly helped disrupt this industry in multiple avenues. It has enhanced security to better safeguard assets by analyzing large volumes of security data to identify fraudulent behavior, suspicious transactions and potential future attacks. Document processing is a key activity in financial services. It involves time, is prone to human error and vulnerable to duplications. AI speeds up the processing time and reduces the errors significantly. However, the most valuable benefit is ‘data’. The future of financial services is mostly reliant on acquiring data to stay ahead of competition, here AI plays a significant role. Powered by AI, organizations can process massive volume of data, this will offer them game-changing insights that in turn will provide better experience for its customers. Healthcare In healthcare, AI will help identify high risk patient groups, and launch preventive medication for them. Hospitals typically can use AI to both automate and optimize operations. Diagnosis which used to get delayed due to multiple opinions can now become faster and accurate. Healthcare expense can now be accurately estimated with focus on healing. In this journey of healthcare, specialists can now formulate better drugs and dosage, and virtual agents can help deliver a great healing experience. Education In education, AI can connect need with content. It can help identify key drivers of performance for students to highlight and build their strengths. It can personalize learning and shift from break test model to continuous feedback based learning empowered by virtual tutors. It can also automate human tutors’ mundane tasks, detect early disengagement signs in students, and help form groups on focussed learning objectives. Storage Enterprises are rapidly shifting towards cloud storage. Lesser dedicated storage arrays driven by dynamic storage software will now be run by deep learning brains. This will help companies add or remove storage capacity in real time, thus reducing 70 percent in cost. Next generation scale-out computing environments will have a few thousand cores (neurons) and they will be connected at tremendously high speed and at exceptional low latencies. Servers that are part of these neural-class networks are instrumented for the telemetry that is needed to build and automate self-driving data centers. They are instrumented to process packets that are needed for real-time analytics. The key trends that have led to the emergence of “Neural-Class Networks” are the computing environments which are used for AI that uses the distributed scale-out architecture, and data of massive size. They can be found in the data centers service providers in public cloud, exchanges, retailers, financial organizations and large carriers, to handpick a few. The digital enterprises that are successfully flourishing today depend a lot on algorithms, automation, and analytics driven by AI. These emerging technologies which were previously available only to large enterprises have now become accessible and affordable, thanks to democratization of AI. Today even SMBs have the required AI tools, access to skilled AI partners, and the right people to financially back the disruptive ideas that can effectively help them compete with larger players. The exciting times have just begun.

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AI deep learning ML

Aziro (formerly MSys Technologies) 2019 Tech Predictions: Smart Storage, Cloud’s Bull Run, Ubiquitous DevOps, and Glass-Box AI

2019 brings us to the second-last leg of this decade. From the last few years, IT professionals have been propagating rhetoric. They state that the technology landscape is seeing a revolutionary change. But, most of the “REVOLUTIONARY” changes, has, over the time lost their gullibility. Thanks to the awe-inspiring technologies like AI, Robotics, and upcoming 5G networks most tech pundits consider this decade to be a game changer in the technology sector.As we make headway into 2019, the internet is bombarded with numerous tech prophecies. Aziro (formerly MSys Technologies) presents to you the 2019 tech predictions based on our Storage, Cloud, DevOps and digital transformation expertise.1. Software Defined Storage (SDS)Definitely, 2019 looks promising for Software Defined Storage. It’ll be driven by changes in Autonomous Storage, Object Storage, Self-Managed DRaaS and NVMes. But, SDS will also be required to push the envelope to acclimatize and evolve. Let’s understand why so.1.1 Autonomous Storage to Garner MomentumBacked by users’ demand, we’ll witness the growth of self-healing storage in 2019. Here, Artificial Intelligence powered by intelligent algorithms will play a pivotal role. Consequently, companies will strive to ensure uninterrupted application performance, round the clock.1.2 Self-Managed Disaster Recovery as a Service (DRaaS) will be ProminentSelf-Managed DRaaS reduces human interference and proactively recovers business-critical data. It then duplicates the data in the Cloud. This brings relief during an unforeseen event. Ultimately, it cuts costs. In 2019, this’ll strike chords with enterprises, globally, and we’ll witness DRaaS gaining prominence.1.3 The Pendulum will Swing Back to Object Storage as a Service (STaaS)Object Storage makes a perfect case for cost-effective storage. Its flat structure creates a scale-out architecture and induces Cloud compatibility. It also assigns unique Metadata and ID for each object within storage. This accelerates the data retrieval and recovery process. Thus, in 2019, we expect companies to embrace Object Storage to support their Big data needs.1.4 NMVes Adoption to Register TractionIn 2019, Software Defined Storage will accelerate the adoption rate of NVMes. It rubs off glitches associated with traditional storage to ensure smooth data migration while adopting NVMes. With SDS, enterprises need not worry about the ‘Rip and Replace’ hardware procedure. We’ll see vendors design storage platforms that append to NVMes protocol. For 2019, NMVes growth will mostly be led by FC-NVME and NVMe-oF.2. Hyperconverged Infrastructure (HCI)In 2019, HCI will remain the trump card to create a multi-layer infrastructure with centralized management. We’ll see more companies utilize HCI to deploy applications quickly. This’ll circle around a policy-based and data-centric architecture.3. Hybridconverged Infrastructure will Mark its FootprintHybridconverged Infrastructure (HCI.2) comes with all the features of its big brother – Hyperconverged Infrastructure (HCI.1). But, one extended functionality makes the latter smarter. Unlike HCI.1, it allows connecting with an external host. This’ll help HCI.2 mark its footprint in 2019.4. VirtualizationIn 2019, Virtualization’s growth will be centered around Software Defined Data Centers and Containers.4.1 ContainersContainer technology is ace in the hole to deliver promises of multi-cloud – cost efficacy, operational simplicity, and team productivity. Per IDC, 76 percent of users’ leverage containers for mission-critical applications.4.1.1 Persistent Storage will be a Key ConcernIn 2019, Containers’ users will envision a cloud-ready persistent storage platform with flash arrays. They’ll expect their storage service providers to implement synchronous mirroring, CDP – continuous data protection and auto-tiering.4.1.2 Kubernetes Explosion is ImminentThe upcoming Kubernetes version is rumored to include a pre-defined configuration template. If true, it’ll enable an easier Kubernetes deployment and use. This year, we are also expecting a higher number of Kubernetes and containers synchronization. This’ll make Kubernetes’ security a burgeoning concern. So, in 2019, we should expect stringent security protocols around Kubernetes deployment. It can be multi-step authentication or encryption at the cluster level.4.1.3 Istio to Ease Kubernetes Deployment HeadacheIstio is an open source service mesh. It addresses the Microservices’ application deployment challenges like failure recovery, load balancing, rate limiting, A/B testing, and canary testing. In 2019, companies might combine Istio and Kubernetes. This can facilitate a smooth Container orchestration, resulting in an effortless application and data migration.4.2 Software Defined Data CentersMore companies will embark on their journey to Multi-Cloud and Hybrid-Cloud. They’ll expect a seamless migration of existing applications to a heterogeneous Cloud environment. As a result, SDDC will undergo a strategic bent to accommodate the new Cloud requirements.In 2019, companies will start cobbling DevOps and SDDC. The pursuit of DevOps in SDDC will be to instigate a revamp of COBIT and ITIL practice. Frankly, without wielding DevOps, cloud-based SDDC will remain in a vacuum.5. DevOpsIn 2019, companies will implement a programmatic DevOps approach to accelerate the development and deployment of software products. Per this survey, DevOps enabled 46x code deployment. It also skyrocketed the deploy lead time by 2556x. This year, AI/ML, Automation, and FaaS will orchestrate changes to DevOps.5.1 DevOps Practice Will Experience a Spur with AI/MLIn 2019, AI/ML centric applications will experience an upsurge. Data science teams will leverage DevOps to unify complex operations across the application lifecycle. They’ll also look to automate the workflow pipeline – to rebuild, retest and redeploy, concurrently.5.2 DevOps will Add Value to Functions as a Service (FaaS)Functions as a Service aims to achieve serverless architecture. It leads to a hassle-free application development without perturbing companies to handle the monolithic REST server. It is like a panacea moment for developers.Hitherto, FaaS hasn’t achieved a full-fledged status. Although FaaS is inherently scalable, selecting wrong user cases will increase the bills. Thus, in 2019, we’ll see companies leveraging DevOps to fathom productive user cases and bring down costs drastically.5.3 Automation will be the Mainstream in DevOpsManual DevOps is time-consuming, less efficient, and error-prone. As a result, in 2019, CI/CD automation will become central in the DevOps practice. Consequently, Infrastructure as a Code to be in the driving seat.6. Cloud’s Bull Run to ContinueIn 2019, organizations will reimagine the use of Cloud. There will be a new class of ‘born-in-cloud’ start-ups, that will extract more value by intelligent Cloud operations. This will be centered around Multi-Cloud, Cloud Interoperability, and High Performance Computing. More companies will look to establish a Cloud Center of Excellence (CoE). Per RightScale survey, 57 percent of enterprises already have a Cloud Center of Excellence.6.1 Companies will Drift from “One-Cloud Approach.”In 2018, companies realized that having a ‘One-Cloud Approach’ encumbers their competitiveness. In 2019, Cloud leadership teams will bask upon the Hybrid-Cloud Architecture. Hybrid-Cloud will be the new normal within Cloud Computing in 2019.6.2 Cloud Interoperability will be a Major ConcernIn 2019, companies will start addressing the issues of interoperability by standardizing Cloud architecture. The use of the Application Programming Interface (APIs) will also accelerate. APIs will be the key to instill the capability of language neutrality, which augments system portability.6.3 High Performance Computing (HPC) will Get its Place on CloudIndustries such as Finance, Deep Learning, Semiconductors or Genomics are facing the brunt of competition. They’ll envision to deliver high-end compute-intensive applications with high performance. To entice such industries, Cloud providers will start imparting HPC capabilities in their platform. We’ll also witness large scale automation in Cloud.7. Artificial IntelligenceFor 2019 AI/ML will come out of the research and development model to be widely implemented in organizations. Customer engagements, infrastructure optimization, and Glass-Box AI, will be in the forefront.7.1 AI to Revive Customer EngagementsBusinesses (startups or enterprise) will leverage AI/ML to enable a rich end-user experience. Per Adobe, enterprises using AI will more than double in 2019. Tech and non-tech companies, alike, will strive to offer personalized services leveraging Natural Language Processing. The focus will remain to create a cognitive customer persona to generate tangible business impacts.7.2 AI for Infrastructure OptimizationIn 2019, there will a spur in the development of AI embedded monitoring tools. This’ll help companies to create a nimble infrastructure to respond to the changing workload. With such AI-driven machines, they’ll aim to cut down the infrastructure latency, infuse robustness in applications, enhance performances, and amplify outputs.7.3 Glass-Box AI will be crucial in Retail, Finance, and HealthcareThis is where Explainable AI will play its role. Glass-Box AI will create key customers’ insights with underlying methods, errors or biases. In this way, retailers don’t necessarily follow every suggestion. They can sort out responses that fit rights in that present scenario. The bottom-line will be to avoid customer altercations and bring out fairness in the process.

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AI automation Autonomous Storage Cloud Interoperability Containers devops DRaaS Embedded AI FaaS Glass-Box AI

Your 2022 Continuous DevOps Monitoring Solution Needs Pinch Of Artificial Intelligence

DevOps helped technologists save time such drastically that the projects that were barely deployed in a year or more are now seeing the daylight in just months or even weeks. It removed communication bottlenecks, eased the change management, and helped with an end-to-end automation cycle for the SDLC. However, as has been the interesting feature of humanity, any innovation that eases our life also brings with it challenges of its own. Bending over backward, the business leaders now have much more complex customer demands and employee skillset requirements to live up to. Digital Modernization requires rapid and complex processes that move along the CI/CD pipeline with all sorts of innovative QA automation, Complex APIs, Configuration Management Platforms, and Infrastructure-as-a-Code, among other dynamic technology integrations. Such complexities are making DevOps turn on its head due to a serious lack of visibility over the workloads. It is, therefore, time for the companies to put their focus to an essential part of their digital transformation journey – the Monitoring. Continuous Monitoring for the DevOps of Our Times DevOps monitoring is a proactive approach that helps us detect the defects in the CI/CD pipeline and strategize to resolve them. Moreover, a good monitoring strategy can curb potential failures even before they occur. In other words, one cannot hold the essence of DevOps frameworks with their time-to-market benefits without having a good monitoring plan. With the IT landscape getting more and more unpredictable with each day, even DevOps monitoring solutions need to evolve into something more dynamic than its traditional ways. Therefore, it is time for global enterprises and ISVs to adopt Continuous Monitoring. Ideally, Continuous Monitoring or Continuous Control Monitoring in DevOps refers to end-to-end monitoring of each phase in the DevOps pipeline. It helps DevOps teams gain insight into the CI/CD processes for their performance, compliance, security, infrastructure, among others, by offering useful metrics and frameworks. The different DevOps phases can be protected with easy threat assessments, quick incident responses, thorough root cause analysis, and continuous general feedback. In this way, Continuous Monitoring covers all three pillars of a contemporary software – Infrastructure, Application, and Network. It is capable of reducing system downtimes by rapid responses, full network transparency and proactive risk management. There’s one more technology that the technocrats handling the DevOps of our times are keen to work on – Artificial Intelligence (AI). So it wouldn’t be a surprise if the conversations about Continuous Monitoring being fuelled by AI are already brewing up. However, such dream castles need a concrete technology-rich floor. Therefore, we will now look at the possibilities for implementing Continuous DevOps Monitoring Solutions with Artificial Intelligence holding the reins. Artificial Intelligence for Continuous Monitoring As discussed above Continuous Monitoring essentially promises the health and performance efficiency of the infrastructure, application, and network. There are solutions like Azure DevOps Monitoring, AWS DevOps monitoring and more that offer surface visibility dashboards, custom monitoring metrics, hybrid cloud monitoring, among other benefits. So, how do we weave in Artificial Intelligence into such tools and technologies? It mainly comes down to collecting, analyzing, and processing the monitoring data coming in from the various customized metrics. In fact, a more liberal thought can be given even to accommodate setting up these metrics throughout the different phases of DevOps. So, here’s how Artificial Intelligence can help with Continuous Monitoring and empower the DevOps teams to navigate the complex nature of modern applications. Proactive Monitoring AI can enable the DevOps pipeline to quickly analyze the data coming in from monitoring tools and raise real-time notifications for any potential downtime issues or performance deviations. Such analysis might exhaust much more manual workforce than AI-based tools that can automatically identify and update about unhealthy system operations much more frequently and efficiently. Based on the data analysis, they can also help customize the metrics to look for more vulnerable performance points in the CI/CD pipeline for a more proactive response. Resource-Oriented Monitoring One of the biggest challenges while implementing Continuous Monitoring is the variety of infrastructure and networking resources used for the application. The uptime checks, on-premise Monitoring, component health checks are different in Hybrid cloud and Multi-cloud environments. Therefore, monitoring such IT stacks and for an end-to-end DevOps might be a bigger hassle than one can imagine. However, AI-based tools can be programmed to find unusual patterns even in such complex landscapes by tracking various system baselines. Furthermore, AI can also quickly pin-point the specific defective cog in the wheel that might be holding the machinery down. Technology Intelligence The built-in automation and proactiveness of Artificial Intelligence enables it to relax the workforce and the system admins by identifying and troubleshooting the complicated systems. Whether it is a Kubernetes cluster, or a malfunctioning API, AI can support the monitoring administrators to have an overall visibility and make informed decisions about the DevOps apparatus. Such technology intelligence would otherwise require a very unique skillset that might be too easy to hire or acquire. Therefore, enterprises and ISVs can turn to AI for empowering their DevOps monitoring solutions and teams with the required support. Conclusion DevOps is entering the phase of specializations. AIOps, DevSecOps, InfraOps and more are emerging to help the industries with their specific and customized DevOps automation needs. Therefore, it is necessary that the DevOps teams have the essential monitoring resources to ensure minimal to no failures. Continuous Monitoring aided by Artificial Intelligence can provide the robust mechanism that would help the technology experts mitigate the challenges of navigating the complex digital landscape thus, helping the global industries with their digital transformation ambitions.

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Aziro's AI Vision: A Look Into the Future of Intelligent Enterprises

In an era characterized by perpetual innovation, AI has evolved from being an experimental frontier to becoming the foundation of competitiveness. Enterprises worldwide are adopting AI capabilities, such as predictive analytics, cognitive automation, and self-healing infrastructure, to strengthen their digital foundations. However, this shift requires more than simply adding bolt-on machine learning tools. It requires an AI-native engineering model built into every layer of the tech stack. Originally MSys Technologies, Aziro changed its name in June 2025 to reflect its AI philosophy. From a new mission to provide AI‑powered solutions and from autonomous CI/CD pipelines to explainable machine‑learning frameworks, Aziro is at the forefront of the intelligent‑enterprise wave. What’s the future of AI with Aziro? As AI models become increasingly complex and data volumes reach petabyte scale, enterprises face significant challenges in deployment, governance, and long-term maintenance. Aziro’s vision for the future addresses these challenges head-on by infusing intelligence in three strategic pillars: Predictive Infrastructure: By learning from past metrics and anomaly-detection algorithms, platforms can proactively address problems, scaling services automatically ahead of spikes in load or rerouting traffic when latency limits are exceeded. This “self‑healing” template minimizes downtime, allowing engineers to focus on experimentation rather than firefighting. AI-Augmented DevOps: Conventional pipelines often rely on human-driven approval gates and explicit rollback steps. Aziro’s future‑ready CI/CD brings ML-driven risk scoring at every step, suggesting rollbacks or canary-deploy approaches automatically. Explainable AI & Compliance: With regulators demanding transparency in AI decisions, Aziro ensures model predictions can be traced and explained, whether it’s a loan approval or a medical diagnosis. Developers gain clear visibility into how models make decisions, with tools that enable them to examine key features and trace every step of the inference process. Combined, these pillars create a harmonious blueprint for an AI‑first world, a world where intelligence is not an add-on but the very texture of software and infrastructure. What is the role of AI in Aziro’s products? Aziro’s automation test suite, designed to accelerate digital transformation, comprises three exemplary tools: Mobitaz, MTAS, and PurpleStrike RT. All three are feature-rich intelligent automation tools to enhance developer productivity and application quality. Mobitaz: Mobitaz is an advanced test automation solution for native and hybrid Android applications, designed for Android/iOS. Mobitaz supports concurrent execution across many Android devices, OS platforms, and configurations, providing quick and consistent test coverage for the mobile universe. With built-in system resource performance reporting, Mobitaz enables engineering teams to identify defects early, reduce QA cycles, and deploy mobile apps more quickly and confidently. MTAS: MTAS is Aziro’s end-to-end automation platform with Microsoft Visual Studio .NET integration. It offers record-and-playback testing against web, Windows, Flex, Siebel, and SSH interfaces. Its data-driven testing (XML, CSV, and XLS support), automated email report, and script-free multi-browser support facilities allow enterprise QA teams to run repeatable, scalable tests across platforms with minimal human intervention. PurpleStrike RT: PurpleStrike RT is Aziro’s cloud-based, real-time load testing platform. It emulates traffic from actual browsers on AWS EC2 to create realistic load profiles. PSRT’s distributed architecture minimizes the threat of application failure under load, and its automations enable teams to detect scalability problems earlier in the SDLC. With improved ROI and quicker go-to-market results, PurpleStrike RT becomes vital to performance assurance in the current high-demand software world. Through these products, Aziro integrates intelligence directly into your workflows, relieving pressure from human-driven testing and shifting it to AI-powered orchestration. Developers save time, quality is enhanced, and feedback loops get tighter. What benefits does Aziro bring to enterprises? By embracing an AI-native mentality, organizations can achieve real benefits throughout the software lifecycle and operational spaces: Less Operational Overhead: Auto-scaling, auto-healing, and compliance scans reduce human tickets and eliminate firefighting. Early customers experience a 30–50% decrease in mundane support workloads, enabling teams to concentrate on building features. Faster Time to Market: Predictive risk scoring pipelines reduce rollbacks and speed up approval processes. Companies adopting these revolutionary pipelines have seen a double increase in deployment frequency, with no decrease in system stability and, in some cases, an improvement in performance. Enhanced Reliability & Resilience: With AI-based anomaly detection and self-correction, systems reach greater uptime and elegant degradation under load. For mission-critical systems, this directly conveys that it offers more dependable and consistent performance and increased customer satisfaction. Streamlined Compliance: Integrated policy engines and explainable AI artifacts simplify audits and enhance transparency. From GDPR to HIPAA compliance, or from industry-specific norms, businesses gain real-time visibility into model decisions and infrastructure modifications, reducing compliance expenses by as much as 40%. Insights Powered by Data: Built-in BI tools deliver automated reports, trend analysis, and actionable insights, empowering product and operations teams to make smarter decisions more quickly. By unlocking these advantages, companies not only upgrade their technology stack but also build a culture of ongoing improvement, where data and automation fuel every action in the value chain. Wrapping Up The convergence of AI and enterprise software development marks a revolutionary age, one where intelligence and automation are no longer separate from the development cycle. As a beacon of this revolution, Aziro best represents how a strategic rebranding can be an outward expression of an internal, more fundamental movement toward AI‑native innovation. From predictive infrastructure to explainable AI compliance, the pillars of Aziro’s vision outline a comprehensive framework for building tomorrow’s innovative enterprises. By building on these understandings and frameworks, developers can design systems that are not only robust and scalable but also continuously shifting, unlocking in full the potential of AI in the enterprise.

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Redefining Infrastructure With AI: Aziro’s Approach to Scalability and Resilience

In today’s increasingly digital age, organizations face mounting challenges in managing large amounts of data, irregular workload fluctuations, and highly sophisticated technological ecosystems. The conventional infrastructure management turns out to be inadequate, incapable of delivering the required flexibility, responsiveness, or scalability to address such changing needs. Aziro, the innovator in AI-native engineering, meets these pivotal challenges through deeply embedding artificial intelligence into infrastructure management. Aziro’s cutting-edge solutions enable businesses with record-breaking scalability, strong operational resilience, and impressive system reliability, guaranteeing success in the face of technological changes and increasing business complexities. Can Aziro Improve Your Cloud Infrastructure? Cloud infrastructure is the virtual underpinning of modern-day organizations. It carries mission-critical applications, fuels real-time analytics, and enables dispersed teams. Yet, conventional cloud configurations tend to run with static provisioning designs, where resources are provisioned according to forecasted demand. This leads to over-provisioning (and unnecessary spending) or under-provisioning (and resulting slowdowns and downtime). That’s where Aziro’s AI-first architecture provides a significant step up. By integrating intelligent analytics into cloud operations, Aziro enables the analysis of workloads in real-time and the automatic scaling of resources. Consider a platform that not only identifies a traffic surge before it occurs but also auto-provisions resources to accommodate it, with no human intervention needed. This decreases the mean time to recovery (MTTR) and allows IT organizations to concentrate on long-term gains instead of firefighting. Aziro also supports robust infrastructure scaling during large-scale changes, whether it’s adding thousands of new users or moving legacy infrastructure. Their platform is easily integrated with CI/CD pipelines, infrastructure-as-code tools, and observability platforms. With this, change is no longer dangerous; it’s routine and controllable. How Can Aziro Help With Operational Resilience? Operational resilience is not only about responding to issues; it’s also about preventing them. Whatever the cause, regional outages, hardware malfunctions, software glitches, and unplanned downtime can lead to revenue loss, compliance penalties, and damage to the brand. Aziro bolsters business continuity through proactive, AI-driven infrastructure. A top value in Aziro’s resilience strategy is predictive incident management. By leveraging telemetry from throughout the tech stack, servers, VMs, containers, databases, Aziro trains its AI models to identify early warning signs of system deterioration. That means problems such as memory leaks, CPU spikes, or latency buildup can be corrected before users are affected. Self-healing automation adds yet another level of resilience. When problems are detected, Aziro’s system can automatically reroute traffic, create backup instances, or recover services from snapshots, eliminating the need for human intervention. This minimizes mean time to recovery (MTTR) and allows IT teams to prioritize long-term enhancements instead of firefighting. Aziro also enables resilient infrastructure scaling through large-scale changes, whether it’s adding thousands of new users or migrating legacy infrastructure. Their platform seamlessly integrates with CI/CD pipelines, infrastructure-as-code tools, and observability platforms. Change is no longer perilous — it’s standard operating procedure. How Does Aziro Enhance System Reliability? For nearly every company today, reliability is a currency. When your platform crashes, not only do you lose customers, but you also lose trust. As digital experience has become a leading brand differentiator, system reliability needs to be built into the core of the infrastructure. Aziro introduces AI to this equation by using predictive maintenance principles for IT systems. Like how smart factories today utilize machine learning and sensors to prevent equipment failures, Aziro applies AI to analyze logs, system metrics, and past incidents. The outcome? Downtime is no longer unexpected, it’s avoided thanks to smart planning. Anomaly detection is an important piece. Aziro constantly measures real-time system performance against learned baselines. When something is different, such as an API that begins slowing down or a database encountering unusual query volume, the system signals it and automatically starts triaging the problem. In most instances, it can even fix the problem by tweaking the configuration, clearing queues, or scaling resources. Yet another space where Aziro excels is load balancing and traffic distribution. With AI used to track user habits and system latency, it dynamically directs traffic to the healthiest, most responsive nodes. It doesn’t merely avoid outages, it also enhances overall user experience by removing latency and jitter. In regulated industries or mission-critical environments, Aziro also simplifies compliance. With audit logs, policy automation, and encrypted backup capabilities out of the box, it helps businesses remain compliant with HIPAA, GDPR, ISO 27001, and other standards, without overloading IT staff with manual audits. Why Does This Matter More Than Ever? We’re living through a time when digital infrastructure is both a competitive advantage and a potential vulnerability. The organizations that succeed are those that can scale intelligently, recover quickly, and operate with reliability across every layer of their stack. Aziro enables this by turning infrastructure into a living, learning system —one that adapts in real-time and improves continuously. Rather than adding AI to the solutions generated after the fact, Aziro has designed infrastructure anew. The company integrates AI into provisioning, monitoring, security, and maintenance, designing a single system that is much more than the sum of its components. The effect is real, from saving cloud expenses by as much as 30% to cutting incident response time by 50% and enhancing uptime in hybrid environments. Aziro is enabling companies to move smarter and faster. Not only to ride out digital disruption, but to drive it. To Sum Up Redefining infrastructure is more than just a catchphrase; it’s imperative. As organizations expand their online presence, their infrastructure must also evolve from a reactive system to an active partner. Aziro makes this vision a reality by marrying AI with the very fabric of infrastructure planning. With dynamic cloud optimization, inherent resilience, and innovative reliability models, Aziro is empowering forward-looking companies to scale boldly into the future. Whether you’re a rapidly expanding startup or an enterprise transforming legacy infrastructure, Aziro provides the smarts and infrastructure to enable you to succeed.

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Why Aziro’s AI-Native DevOps is the Future of Reliable Systems

The quest for always-on digital services has pushed DevOps far beyond its original goal of faster releases. Modern teams must also deliver resilience, security, and real-time adaptability. One company has re-imagined this landscape by baking intelligence into every layer of the software-delivery pipeline. Aziro couples classic DevOps culture with machine-learning models that predict issues before they arise, recommend the safest deployment path, and even trigger self-healing actions when anomalies are detected. First adopted by fast-moving ISVs, its AI-native approach is now influencing enterprises that cannot afford downtime or slow recovery times. More importantly, the platform treats AI as a first-class citizen rather than a plug-in. Telemetry from code, infrastructure, and user behavior is processed continuously, creating a feedback loop that learns, adapts, and optimizes without manual tuning. The result is a delivery engine that grows smarter with every commit and every incident, steadily shrinking the gap between code and customer value. How does Aziro integrate AI with DevOps? Continuous integration and continuous delivery generate millions of data points each day—from build logs and static-analysis results to real-time performance counters flowing out of staging clusters. Turning that torrent of data into actionable insights begins with disciplined data engineering. All records are normalised into a high-density feature store where they are timestamped, enriched with contextual metadata, and made instantly available to an ensemble of diagnostic models. Classification pipelines separate harmless noise from genuine risk, allowing defects to be identified and trapped long before they reach production. At this stage, the platform, branded as Aziro within customer dashboards, assembles a composite risk score for each commit. From there, a reinforcement-learning policy orchestrator evaluates live traffic from canary environments, continuously adjusting route percentages so end-users always experience the most stable version available. If outlier error rates begin to climb, the orchestrator triggers an automated rollback, explains the root cause in plain language, and opens a remediation ticket linking directly to the offending commit. Infrastructure-as-Code repositories are scanned in parallel; whenever drift is detected, an auto-generated pull request proposes the recommended state, keeping human owners fully in control. Once code reaches the main branch, a topology-aware pipeline graph selects the most efficient execution plan, grouping container builds by dependency so that identical layers are compiled only once. Edge cache invalidations are orchestrated automatically, ensuring that fresh binaries propagate through CDN nodes without human intervention. This end-to-end choreography drastically shortens cycle time while preserving strict traceability for every artefact. How does Aziro Enhance System Reliability? Site Reliability Engineering inside the platform begins with exhaustive observability. Every service call is tracked, every metric is tagged with business context, and every dependency is mapped, enabling the modeling of cascading risks in advance. Predictive analytics engines then scan those signals for precursor patterns—subtle increases in garbage-collection pauses, widening latency histograms, or fan-in spikes that foreshadow resource starvation. Engineers receive hourly posture reports that translate technical drift into potential financial impact, making error budgets tangible for non-technical stakeholders. When an alert exceeds the established budget, an incident graph engine springs into action. It correlates telemetry with historical remediation logs, producing a ranked shortlist of suspected failure domains. First responders see a clear decision tree: which node to inspect, which configuration to revert, and which mitigation playbook has the highest probability of success. Guided triage slashes mean time to acknowledgement and buys breathing room for deeper root-cause analysis. In parallel, a chaos-experimentation scheduler continuously probes the production-grade staging environment. Each experiment is chosen by a weighted algorithm that balances learning value against potential disruption, ensuring high-impact scenarios are tested early and often. Results flow into a resilience knowledge base so future releases inherit the defences learned from previous shocks. In addition, an auto-tuned recovery planner generates simulated rollback scripts for every central subsystem at the moment of deployment, guaranteeing that responders have a proven fallback long before any incident strikes. What is the role of AI in Aziro’s products? Beyond pipelines and infrastructure, the organisation embeds intelligence into standalone offerings that customers can plug into their ecosystems. Aziro doesn’t just use AI to enhance workflows; it builds entire product experiences around it. Mobitaz, for example, provides continuous mobile app test automation by mapping every test flow to device interactions, OS-specific behaviors, and usage patterns. MTAS, a lightweight and scriptless test automation engine, leverages AI to identify UI objects and automatically heal broken test cases, helping QA teams keep pace with frequent changes. PurpleStrike RT, focused on real-time performance testing, uses AI to model user load, detect potential bottlenecks, and adapt test conditions dynamically. These products share a common design philosophy: an explainable core, open APIs, and a learning loop that personalises recommendations to each environment. Over the past hundred words, we have maintained distance from the keyword while outlining product strategy. The architecture under the hood is also composable; models are deployed as microservices wrapped with feature flags, allowing teams to adopt new capabilities incrementally without compromising stability. To Wrap Up After surveying the practice and the platform, it is clear that Aziro has moved DevOps into the age of learning systems. By combining continuous delivery, site reliability engineering, and purpose-built AI products, the company delivers faster feedback, lower incident counts, and infrastructure that fixes itself before customers ever notice a glitch. For leaders evaluating how to modernise their delivery stacks, AI-native DevOps is no longer a research topic. Also, it is a proven route to resilient, scalable software that keeps pace with business ambition.

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