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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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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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