AI in Healthcare

The Healthcare Industry is going through a quiet revolution. Factors like disease trends, doctor demographics, regulatory policies, environment, technology etc. are forcing the industry to turn to emerging technologies like AI, to help adapt to the pace of change. Here, we take a look at some key use cases of AI in Healthcare.

Medical Imaging

The application of Machine Learning (ML) in Medical Imaging is showing highly encouraging results. ML is a subset of AI, where algorithms and models are used to help machines imitate the cognitive functions of the human brain and to also self-learn from their experiences.

AI can be gainfully used in the different stages of medical imaging- in acquisition, image reconstruction, processing, interpretation, storage, data mining & beyond. The performance of ML computational models improves tremendously as they get exposed to more & more data and this foundation on colossal amounts of data enables them to gradually better humans at interpretation. They begin to detect anomalies not perceptible to the human eye & not discernible to the human brain!

What goes hand-in-hand with data, is noise. Noise creates artifacts in images and reduces its quality, leading to inaccurate diagnosis. AI systems work through the clutter and aid noise- reduction leading to better precision in diagnosis, prognosis, staging, segmentation and treatment.

At the forefront of this use case is Radio genomics- correlating cancer imaging features and gene expression. Needless to say, this will play a pivotal role in cancer research.

Drug Discovery

Drug Discovery is an arduous process that takes several years from the start of research to obtaining approval to market. Research involves laboring through copious amounts of medical literature to identify the dynamics between genes, molecular targets, pathways, candidate compounds. Sifting through all of this complex data to arrive at conclusions is an enormous challenge. When this voluminous data is fed to the ML computational models, relationships are reliably established. AI powered by domain knowledge is slashing down time & cost involved in new drug development.

Cybersecurity in Healthcare

Data security is of paramount importance to Healthcare providers who need to ensure confidentiality, integrity, and availability of patient data. With cyberattacks increasing in number and complexity, these formidable threats are giving security teams sleepless nights! The main strength of AI is its ability to curate massive quantities of data- here threat intelligence, nullify the noise, provide instant insights & self-learn in the process. Predictive & Prescriptive capabilities of these computational models drastically reduces response time.

Virtual Health assistants

Virtual Health assistants like Chatbots, give patients 24/7 access to critical information, in addition to offering services like scheduling health check-ups or setting up appointments. AI- based platforms for wearable health devices and health apps come armed with loads of features to monitor health signs, daily activities, diet, sleep patterns etc. and provide alerts for immediate action or suggest personalized plans to enable healthy lifestyles.

AI for Healthcare IT Infrastructure

Healthcare IT Infrastructure running critical applications that enable patient care, is the heart of a Healthcare provider. With dynamically changing IT landscapes that are distributed, hybrid & on-demand, IT Operations teams are finding it hard to keep up. Artificial Intelligence for IT Ops (AIOps) is poised to fundamentally transform the Healthcare Industry. It is powering Healthcare Providers across the globe, who are adopting it to Automate, Predict, Remediate & Prevent Incidents in their IT Infrastructure. GAVS’ Zero Incident FrameworkTM (ZIF) – an AIOps Platform, is a pure-play AI platform based on unsupervised Machine Learning and comes with the full suite of tools an IT Infrastructure team would need. Please watch this video to learn more.

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Analyze

Have you heard of AIOps?

Artificial intelligence for IT operations (AIOps) is an umbrella term for the application of Big Data Analytics, Machine Learning (ML) and other Artificial Intelligence (AI) technologies to automate the identification and resolution of common Information Technology (IT) problems. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor the assets and gain visibility into the working behaviour and dependencies between these assets.

According to a Gartner study, the adoption of AIOps by large enterprises would rise to 30% by 2023.

ZIF – The ideal AIOps platform of choice

Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™

ZIF comprises of 5 modules, as outlined below.

At the heart of ZIF, lies its Analyze and Predict (A&P) modules which are powered by Artificial Intelligence and Machine Learning techniques. From the business perspective, the primary goal of A&P would be 100% availability of applications and business processes.

Come, let us understand more about the Analyze function of ZIF.

With Analyzehaving a Big Data platform under its hood, volumes of raw monitoring data, both structured and unstructured, can be ingested and grouped to build linkages and identify failure patterns.

Data Ingestion and Correlation of Diverse Data

The module processes a wide range of data from varied data sources to break siloes while providing insights, exposing anomalies and highlighting risks across the IT landscape. It increases productivity and efficiency through actionable insights.

  • 100+ connectors for leading tools, environments and devices
  • Correlation and aggregation methods uncover patterns and relationships in the data

Noise Nullification

Eliminates duplicate incidents, false positives and any alerts that are insignificant. This also helps reduce the Mean-Time-To-Resolution and event-to-incident ratio.

  • Deep learning algorithms isolate events that have the potential to become incidents along with their potential criticality
  • Correlation and Aggregation methods group alerts and incidents that are related and needs a common remediation
  • Reinforcement learning techniques are applied to find and eliminate false positives and duplicates

Event Correlation

Data from various sources are ingested real-time into ZIF either by push or pull mechanism. As the data is ingested, labelling algorithms are run to label the data based on identifiers. The labelled data is passed through the correlation engine where unsupervised algorithms are run to mine the patterns. Sub-sequence mining algorithms help in identifying unique patterns from the data.

Unique patterns identified are clustered using clustering algorithms to form cases. Every case that is generated is marked by a unique case id. As part of the clustering process, seasonality aspects are checked from historical transactions to derive higher accuracy of correlation.

Correlation is done based on pattern recognition, helping to eliminate the need for relational CMDB from the enterprise. The accuracy of the correlation increases as patterns reoccur. Algorithms also can unlearn patterns based on the feedback that can be provided by actions taken on correlation. As these are unsupervised algorithms, the patterns are learnt with zero human intervention.

Accelerated Root Cause Analysis (RCA)

Analyze module helps in identifying the root causes of incidents even when they occur in different silos. Combination of correlation algorithms with unsupervised deep learning techniques aid in accurately nailing down the root causes of incidents/problems. Learnings from historical incidents are also applied to find root causes in real-time. The platform retraces the user journeys step-by-step to identify the exact point where an error occurs.

Customer Success Story – How ZIF’s A&P transformed IT Operations of a Manufacturing Giant

  • Seamless end-to-end monitoring – OS, DB, Applications, Networks
  • Helped achieve more than 50% noise reduction in 6 months
  • Reduced P1 incidents by ~30% through dynamic and deep monitoring
  • Achieved declining trend of MTTR and an increasing trend of Availability
  • Resulted in optimizingcommand centre/operations head count by ~50%
  • Resulted in ~80% reduction in operations TCO

For more detailed information on GAVS’ Analyze, or to request a demo please visit zif.ai/products/analyze

References: www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

ABOUT THE AUTHOR

Vasudevan Gopalan


Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.

Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.

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Proactive Monitoring

Is your IT environment proactively monitored?

It is important to have the right monitoring solution for an enterprise’s IT environment. More than that, it is imperative to leverage the right solution and deploy it for the appropriate requirements. In this context, the IT environment includes but is not limited to Applications, Servers, Services, End-User Devices, Network devices, APIs, Databases, etc. Towards that, let us understand the need and importance of Proactive Monitoring. This has a direct role in achieving the journey towards Zero Incident EnterpriseTM. Let us unravel the difference between reactive and proactive monitoring.

Reactive Monitoring – When a problem occurs in an IT environment, it gets notified through monitoring and the concerned team acts on it to resolve the issue.The problem could be as simple as slowness/poor performance, or as extreme as the unavailability of services like web site going down or server crashing leading to loss of business and revenue.  

Proactive Monitoring – There are two levels of proactive monitoring, 

  • Symptom-based proactive monitoring is all about identifying the signals and symptoms of an issue in advance and taking appropriate and immediate action to nip the root-cause in the bud.
  • Synthetic-based proactive monitoring is achieved through Synthetic Transactions. Performance bottlenecks or failures are identified much in advance; even before the actual user or the dependent layer encounters the situation

Symptom-based proactive monitoring is a USP of the ZIF Monitor module. For example, take the case of CPU related monitoring. It is common to monitor the CPU utilization and act based on that. But Monitor doesn’t just focus on CPU utilization, there are a lot of underlying factors which causes the CPU utilization to go high. To name a few,

  • Processor queue length 
  • Processor context switches
  • Processes that are contributing to high CPU utilization

It is important to arrest these brewing factors at the right time, i.e., in the case of Processor Queue length, continuous or sustained queue of greater than 2 threads is generally an indication of congestion at processor level.Of course, in a multiple processor environment, we need to divide the queue length by the number of processors that are servicing the workload. As a remedy, the following can be done

1) the number of threads can be limited at the application level

2) unwanted processes can be killed to help close the queued items

3) upgrading the processor will help in keeping the queue length under control, which eventually will control the CPU utilization.

Above is a sample demonstration of finding the symptom and signal and arrest them proactively. ZIF’s Monitor not only monitors these symptoms, but also suggests the remedy through the recommendation from SMEs.

Synthetic monitoring (SM) is done by simulating the transactions through the tool without depending on the end-user to do the transactions. The advantages of synthetic monitoring are, 

  • it uses automated transaction simulation technology
  • it helps to monitor the environment round-the-clock 
  • it helps to validate from across different geographic locations 
  • it provides options to choose the number of flows/transactions to be verified
  • it is proactive – identifies performance bottlenecks or failures much in advance even before the actual user or the dependent layer encounters the situation

How does Synthetic Monitoring(SM) work?

It works through 3 simple steps,

1) Record key transactions – Any number of transactions can be recorded, if required, all the functional flows can be recorded. An example of transaction in an e-commerce website could be, as simple as login and view the product catalogue, or,as elaborate as login, view product catalogue, move item to cart, check-out, make-payment and logout. For simulation purpose, dummy credit cards are used during payment gateway transactions.

2) Schedule the transactions – Whether it should run every 5 minutes or x hours or minutes.

3) Choose the location from which thesetransactions need to be triggered – The SM is available as on-premise or cloud options. Cloud SM provides the options to choose the SM engines available across globe (refer to the green dots in the figure below).

This is applicable mainly for web based applications, but can also be used for the underlying APIs as well.

SM solution has engines which run the recorded transactions against the target application. Once scheduled, the SM engine hosted either on-premise or remotely (refer to the green dots in the figure shown as sample representation), will run the recorded transactions at a predefined interval. The SM dashboard provides insights as detailed under the benefits section below.

Benefits of SM

As the SM does the synthetic transactions, it provides various insights like,

  • The latency in the transactions, i.e. the speed at which the transaction is happening. This also gives a trend analysis of how the application is performing over a period.
  • If there are any failures during the transaction, SM provides the details of the failure including the stack trace of the exception. This makes fixing the failure simpler, by avoiding the time spent in debugging.
  • In case of failure, SM provides insights into the parameter details that triggered the failure.
  • Unlike real user monitoring, there is the flexibility to test all flows or at least all critical flows without waiting for the user to trigger or experience it.
  • This not only unearths the problem at the application tier but also provides deeper insights while combining it with Application, Server, Database, Network Monitoring which are part of the ZIF Monitor suite.
  • Applications working fine under one geography may fail in a different geography due to various factors like network, connectivity, etc. SM will exactly pinpoint the availability and performance across geographies.

For more detailed information on GAVS’Monitor, or to request a demo please visit, https://zif.ai/products/monitor/

About the Author

Suresh Kumar Ramasamy


Suresh heads the Monitor component of ZIF at GAVS. He has 20 years of experience in Native Applications, Web, Cloud and Hybrid platforms from Engineering to Product Management. He has designed & hosted the monitoring solutions. He has been instrumental in conglomerating components to structure the Environment Performance Management suite of ZIF Monitor.

Suresh enjoys playing badminton with his children. He is passionate about gardening, especially medicinal plants.

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Monitoring Microservices and Containers

Monitoring applications and infrastructure is a critical part of IT Operations. Among other things, monitoring provides alerts on failures, alerts on deteriorations that could potentially lead to failures, and performance data that can be analysed to gain insights. AI-led IT Ops Platforms like ZIF use such data from their monitoring component to deliver pattern recognition-based predictions and proactive remediation, leading to improved availability, system performance and hence better user experience.

The shift away from monolith applications towards microservices has posed a formidable challenge for monitoring tools. Let’s first take a quick look at what microservices are, to understand better the complications in monitoring them.

Monoliths vs Microservices

A single application(monolith) is split into a number of modular services called microservices, each of which typically caters to one capability of the application. These microservices are loosely coupled, can communicate with each other and can be deployed independently.

Quite likely the trigger for this architecture was the need for agility. Since microservices are stand-alone modules, they can follow their own build/deploy cycles enabling rapid scaling and deployments. They usually have a small codebase which aids easy maintainability and quick recovery from issues. The modularity of these microservices gives complete autonomy over the design, implementation and technology stack used to build them.

Microservices run inside containers that provide their execution environment. Although microservices could also be run in virtual machines(VMs), containers are preferred since they are comparatively lightweight as they share the host’s operating system, unlike VMs. Docker and CoreOS Rkt are a couple of commonly used container solutions while Kubernetes, Docker Swarm, and Apache Mesos are popular container orchestration platforms. The image below depicts microservices for hiring, performance appraisal, rewards & recognition, payroll, analytics and the like linked together to deliver the HR function.

Challenges in Monitoring Microservices and Containers

Since all good things come at a cost, you are probably wondering what it is here… well, the flip side to this evolutionary architecture is increased complexity! These are some contributing factors:

Exponential increase in the number of objects: With each application replaced by multiple microservices, 360-degree visibility and observability into all the services, their interdependencies, their containers/VMs, communication channels, workflows and the like can become very elusive. When one service goes down, the environment gets flooded with notifications not just from the service that is down, but from all services dependent on it as well. Sifting through this cascade of alerts, eliminating noise and zeroing in on the crux of the problem becomes a nightmare.

Shared Responsibility: Since processes are fragmented and the responsibility for their execution, like for instance a customer ordering a product online, is shared amongst the services, basic assumptions of traditional monitoring methods are challenged. The lack of a simple linear path, the need to collate data from different services for each process, inability to map a client request to a single transaction because of the number of services involved make performance tracking that much more difficult.

Design Differences: Due to the design/implementation autonomy that microservices enjoy, they could come with huge design differences, and implemented using different technology stacks. They might be using open source or third-party software that makes it difficult to instrument their code, which in turn affects their monitoring.

Elasticity and Transience: Elastic landscapes where infrastructure scales or collapses based on demand, instances appear & disappear dynamically, have changed the game for monitoring tools. They need to be updated to handle elastic environments, be container-aware and stay in-step with the provisioning layer. A couple of interesting aspects to handle are: recognizing the difference between an instance that is down versus an instance that is no longer available; data of instances that are no longer alive continue to have value for analysis of operational efficiency or past performance.

Mobility: This is another dimension of dynamic infra where objects don’t necessarily stay in the same place, they might be moved between data centers or clouds for better load balancing, maintenance needs or outages. The monitoring layer needs to arm itself with new strategies to handle moving targets.

Resource Abstraction: Microservices deployed in containers do not have a direct relationship with their host or the underlying operating system. This abstraction is what helps seamless migration between hosts but comes at the expense of complicating monitoring.

Communication over the network: The many moving parts of distributed applications rely completely on network communication. Consequently, the increase in network traffic puts a heavy strain on network resources necessitating intensive network monitoring and a focused effort to maintain network health.

What needs to be measured

This is a high-level laundry list of what needs to be done/measured while monitoring microservices and their containers.

Auto-discovery of containers and microservices:

As we’ve seen, monitoring microservices in a containerized world is a whole new ball game. In the highly distributed, dynamic infra environment where ephemeral containers scale, shrink and move between nodes on demand, traditional monitoring methods using agents to get information will not work. The monitoring system needs to automatically discover and track the creation/destruction of containers and explore services running in them.

Microservices:

  • Availability and performance of individual services
  • Host and infrastructure metrics
  • Microservice metrics
  • APIs and API transactions
    • Ensure API transactions are available and stable
    • Isolate problematic transactions and endpoints
  • Dependency mapping and correlation
  • Features relating to traditional APM

Containers:

  • Detailed information relating to each container
    • Health of clusters, master and slave nodes
  • Number of clusters
  • Nodes per cluster
  • Containers per cluster
    • Performance of core Docker engine
    • Performance of container instances

Things to consider while adapting to the new IT landscape

Granularity and Aggregation: With the increase in the number of objects in the system, it is important to first understand the performance target of what’s being measured – for instance, if a service targets 99% uptime(yearly), polling it every minute would be an overkill. Based on this, data granularity needs to be set prudently for each aspect measured, and can be aggregated where appropriate. This is to prevent data inundation that could overwhelm the monitoring module and drive up costs associated with data collection, storage, and management.    

Monitor Containers: The USP of containers is the abstraction they provide to microservices, encapsulating and shielding them from the details of the host or operating system. While this makes microservices portable, it makes them hard to reach for monitoring. Two recommended solutions for this are to instrument the microservice code to generate stats and/or traces for all actions (can be used for distributed tracing) and secondly to get all container activity information through host operating system instrumentation.    

Track Services through the Container Orchestration Platform: While we could obtain container-level data from the host kernel, it wouldn’t give us holistic information about the service since there could be several containers that constitute a service. Container-native monitoring solutions could use metadata from the container orchestration platform by drilling into appropriate layers of the platform to obtain service-level metrics. 

Adapt to dynamic IT landscapes: As mentioned earlier, today’s IT landscape is dynamically provisioned, elastic and characterized by mobile and transient objects. Monitoring systems themselves need to be elastic and deployable across multiple locations to cater to distributed systems and leverage native monitoring solutions for private clouds.

API Monitoring: Monitoring APIs can provide a wealth of information in the black box world of containers. Tracking API calls from the different entities – microservices, container solution, container orchestration platform, provisioning system, host kernel can help extract meaningful information and make sense of the fickle environment.

Watch this space for more on Monitoring and other IT Ops topics. You can find our blog on Monitoring for Success here, which gives an overview of the Monitorcomponent of GAVS’ AIOps Platform, Zero Incident FrameworkTM (ZIF). You can Request a Demo or Watch how ZIF works here.

About the Author:

Sivaprakash Krishnan


Bio – Siva is a long timer at Gavs and has been with the company for close to 15 years. He started his career as a developer and is now an architect with a strong technology background in Java, Big Data, DevOps, Cloud Computing, Containers and Micro Services. He has successfully designed & created a stable Monitoring Platform for ZIF, and designed & driven cloud assessment and migration, enterprise BRMS and IoT based solutions for many of our customers. He is currently focused on building ZIF 4.0, a new gen business-oriented TechOps platform.

Padmapriya Sridhar


Bio – Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel and Yoga. She aspires to become a Yoga Instructor some day!

A Deep Dive into Deep Learning!

The Nobel Prize winner & French author André Gide said, “Man cannot discover new oceans unless he has the courage to lose sight of the shore”. This rings true with enterprises that made bold investments in cutting-edge AI that are now starting to reap rich benefits. Artificial Intelligence is shredding all perceived boundaries of a machine’s cognitive abilities. Deep Learning, at the very core of Artificial Intelligence, is pushing the envelope still further into unchartered territory. According to Gartner, “Deep Learning is here to stay and expands ML by allowing intermediate representations of the data”.

What is Deep Learning?

Deep Learning is a subset of Machine Learning that is based on Artificial Neural Networks (ANN). It is an attempt to mimic the phenomenal learning mechanisms of the human brain and train AI models to perform cognitive tasks like speech recognition, image classification, face recognition, natural language processing (NLP) and the like.

The tens of billions of neurons and their connections to each other form the brain’s neural network. Although Artificial Neural Networks have been around for quite a few decades now, they are now gaining momentum due to the declining price of storage and the exponential growth of processing power. This winning combination of low-cost storage and high computational prowess is bringing back Deep Learning from the woods.

Improved machine learning algorithms and the availability of staggering amounts of diverse unstructured data such as streaming and textual data, are boosting performance of Deep Learning systems. The performance of the ANN depends heavily on how much data it is trained with and it continuously adapts and evolves its learning with time as it is exposed to more & more datasets.

Simply put, the ANN consists of an Input layer, hidden computational layers, and the Output layer. If there is more than one hidden layer between the Input & Output layers, then it is called a Deep Network.

The Neural Network

The Neuron is central to the human Neural Network. Neurons have Dendrites, which are the receivers of information and the Axon which is the transmitter. The Axon is connected to the Dendrites of other neurons, through which signal transmission takes place. The signals that are passed are called Synapses.

While the neuron by itself cannot accomplish much, it creates magic when it forms connections with the other neurons to form an interconnected neural network. In artificial neural networks, the neuron is represented by a node or a unit. There are several interconnected layers of such units, categorized as input, output and hidden, as seen in the figure. 

A Deep Dive into Deep Learning!

The input layer receives the input values and passes them onto the first hidden layer in the ANN, similar to how our senses receive inputs from the environment around us & send signals to the brain. Let’s look at what happens in one node when it receives these input values from the different nodes of the input layer. The values are standardized/normalized-so that they are all within a certain range-and then weighted. Weights are crucial to a neural network since a value’s weight is indicative its impact on the outcome. An activation function is then applied to the weighted sum of values, to help determine if this transformed value needs to be passed on within the network. Some commonly used activation functions are the Threshold, Sigmoid and Rectifier functions.

This gives a very high-level idea of the generic structure and functioning of an ANN. The actual implementation would use one of several different architectures of neural networks that define how the layers are connected together, and what functions and algorithms are used to transform the input data. To give a couple of examples, a Convolutional network uses nonlinear activation functions and is highly efficient at processing nonlinear data like speech, image and video while a Recurrent network has information flowing around recursively, is much more complicated and difficult to train, but that much more powerful. Recurrent networks are closer in representation to the human neural network and are best suited for applications like sequence generation and predicting stock prices.

Deep Learning at work

Deep Learning has been adopted by almost all industry verticals at least at some level. To give some interesting examples, the automobile industry employs it in self-driving vehicles and driver-assistance services, the entertainment industry applies it to auto-addition of audio to silent movies and social media uses deep learning for curation of content feeds in user’s timelines. Alexa, Cortana, Google Assistant and Siri have now invaded our homes to provide virtual assistance!

Deep Learning has several applications in the field of Computer Vision, which is an umbrella term for what the computer “sees”, that is, interpreting digital visual content like images, photos or videos. This includes helping the computer learn & perform tasks like Image Classification, Object Detection, Image Reconstruction, to name a few. Image classification or image recognition when localized, can be used in Healthcare for instance, to locate cancerous regions in an x-ray and highlight them.

Deep Learning applied to Face Recognition has changed the face of research in this area. Several computational layers are used for feature extraction, with the complexity and abstraction of the learnt feature increasing with each layer, making it pretty robust for applications like public surveillance or public security in buildings. But there are still many challenges like the identification of facial features across styles, ages, poses, effects of surgery that need to be tackled before FR can be reliably used in areas like watch-list surveillance, forensic tasks which demand high levels of accuracy and low alarm rates. 

Similarly, there are several applications of deep learning for Natural Language Processing. Text Classification can be used for Spam filtering, Speech recognition can be used to transcribe a speech, or create captions for a movie, and Machine translation can be used for translation of speech and text from one language to another.

Closing Thoughts

As evident, the possibilities are endless and the road ahead for Deep Learn is exciting! But, despite the tremendous progress in Deep Learning, we are still very far from human-level AI. AI models can only perform local generalizations and adapt to new situations that are similar to past data, whereas human cognition is capable of quickly acclimatizing to radically novel circumstances. Nevertheless, this arduous R&D journey has nurtured a new-found respect for nature’s engineering miracle – the infinitely complex human brain!

Is Your Investment in TRUE AI?

Yes, AIOps the messiah of ITOps is here to stay! The Executive decision now is on the who and how, rather than when. With a plethora of products in the market offering varying shades of AIOps capabilities, choosing the right vendor is critical, to say the least.

Exclusively AI-based Ops?

Simply put, AIOps platforms leverage Big Data & AI technologies to enhance IT operations. Gartner defines Acquire, Aggregate, Analyze & Act as the four stages of AIOps. These four fall under the purview of Monitoring tools, AIOps Platforms & Action Platforms. However, there is no Industry-recognized mandatory feature list to be supported, for a Platform to be classified as AIOps. Due to this ambiguity in what an AIOps Platform needs to Deliver, huge investments made in rosy AIOps promises can lead to sub-optimal ROI, disillusionment or even derailed projects. Some Points to Ponder…

  • Quality in, Quality out. The value delivered from an AIOps investment is heavily dependent on what data goes into the system. How sure can we be that IT Asset or Device monitoring data provided by the Customer is not outdated, inaccurate or patchy? How sure can we be that we have full visibility of the entire IT landscape? With Shadow IT becoming a tacitly approved aspect of modern Enterprises, are we seeing all devices, applications and users? Doesn’t this imply that only an AIOps Platform providing Application Discovery & Topology Mapping, Monitoring features would be able to deliver accurate insights?
  • There is a very thin line between Also AI and Purely AI. Behind the scenes, most AIOps Platforms are reliant on CMDB or similar tools, which makes Insights like Event Correlation, Noise Reduction etc., rule-based. Where is the AI here?
  • In Gartner’s Market Guide, apart from support features for the different data types, Automated Pattern Discovery is the only other Capability taken into account for the Capabilities of AIOps Vendors matrix. With Gartner being one of the most trusted Technology Research and Advisory companies, it is natural for decision makers to zero-in on one of these listed vendors. What is not immediately evident is that there is so much more to AIOps than just this, and with so much at stake, companies need to do their homework and take informed decisions before finalizing their vendor.
  • Most AIOps vendors ingest, provide access to & store heterogenous data for analysis, and provide actionable Insights and RCA; at which point the IT team takes over. This is a huge leap forward, since it helps IT work through the data clutter and significantly reduces MTTR. But, due to the absence of comprehensive Predictive, Prescriptive & Remediation features, these are not end-to-end AIOps Platforms.
  • At the bleeding edge of the Capability Spectrum is Auto-Remediation based on Predictive & Prescriptive insights. A Comprehensive end-to-end AIOps Platform would need to provide a Virtual Engineer for Auto-Remediation. But, this is a grey area not fully catered to by AIOps vendors.  

The big question now is, if an AIOps Platform requires human intervention or multiple external tools to take care of different missing aspects, can it rightfully claim to be true end-to-end AIOps?

So, what do we do?

Time for you to sit back and relax! Introducing ZIF- One Solution for all your ITOps ills!

We have you completely covered with the full suite of tools that an IT infrastructure team would need. We deliver the entire AIOps Capability spectrum and beyond.

ZIF (Zero Incident Framework™) is an AIOps based TechOps platform that enables proactive Detection and Remediation of incidents helping organizations drive towards a Zero Incident Enterprise™.

The Key Differentiator is that ZIF is a Pure-play AI Platform powered by Unsupervised Pattern-based Machine Learning Algorithms. This is what sets us a Class Apart.

  • Rightly aligns with the Gartner AIOps strategy. ZIF is based on and goes beyond the AIOps framework
  • Huge Investments in developing various patented AI Machine Learning algorithms, Auto-Discovery modules, Agent & Agentless Application Monitoring tools, Network sniffers, Process Automation, Remediation & Orchestration capabilities to form Zero Incident Framework™
  • Powered entirely by Unsupervised Pattern-based Machine Learning Algorithms, ZIF needs no further human intervention and is completely Self-Reliant
  • Unsupervised ML empowers ZIF to learn autonomously, glean Predictive & Prescriptive Intelligence and even uncover Latent Insights
  • The 5 Modules can work together cohesively or as independent stand-alone components
  • Can be Integrated with existing Monitoring and ITSM tools, as required
  • Applies LEAN IT Principle and is on an ambitious journey towards FRICTIONLESS IT.

Realizing a Zero Incident EnterpriseTM

The future of AIOps

AIOps or Artificial Intelligence based IT operations is the buzzword that’s capturing the CXO’s interest in organizations worldwide. Why? Because data explosion is here, and the traditional tools and processes are unable to completely handle its creation, storage, analysis and management. Likewise, humans are unable to thoroughly analyze this data to obtain any meaningful insights. IT teams also face the challenging task of providing speed, security and reliability in an increasingly mobile and connected world.

Add to this the complex, manual and siloed processes that the legacy IT solutions offer to the organizations. As a result, the productivity for IT remains low due to their inability to find the exact root cause of incidents. Plus, the business leaders don’t have a 360-degree view of all their IT and business services across the organization.

AIOps is the Future for IT Operations

AIOps platforms are the foundation on which the organizations will project their future endeavors. Advanced machine learning and analytics are the building blocks to enhance their IT operations through a proactive approach towards service desk, monitoring and automation. Using effective data collection methods that utilize real time analytic technologies, AIOps provide insights to impact business decisions.

Successful AIOps implementations depend on key parameters Index (KPIs) whose impact can be seen on performance variation, service degradation, revenue, customer satisfaction and brand image.

All these impacts the organization’s services including but not limited to supply chain, online or digital. One way in which AIOps can deliver a predictive and proactive IT is by decreasing the MTBF (Mean time between failure), MTTD (Mean time to detection), MTTR (Mean time to resolution) and MTTI (Mean time to investigate) factors.

The future of AIOps is already on the way in the below mentioned use cases. There is just the surface with scope for many more use cases to be added in the future.

Capacity planning

Enterprise workloads are moving to the cloud with providers such as AWS, Google and Azure setting up various configurations for running them. The complexity involved increases as new configurations are added by the architects involving parameters like disk types, memory, network and storage resources.

AIOps can reduce the guesswork in aligning the correct usage of the network, storage and memory resources with the right configurations of servers and VMs through recommendations.

Optimal resource utilization

Enterprises are leveraging cloud elasticity to improve their application scaling in or scaling out automatically. With AIOps, IT administrators can rely on predictive scaling to take the auto scale cloud to the next level. Based on historical data, the workload will automatically determine the resources required by monitoring itself.

Data store management

AIOps can also be utilized to monitor the network and the storage resources that will impact the applications in the operations. When performance degradation issues are seen, the admin will get notified. By using AI for both network and storage management, mundane tasks such as reconfiguring and recalibration can be automated. Through predictive analytics, storage capacity is automatically adjusted by adding new volumes proactively.

Anomaly detection

Anomaly detection is the most important application of AIOps. This can prevent potential outages and disruptions that can be faced by organizations. As anomalies can occur in any part of the technology stack, pinpointing them in real-time, using advanced analytics and machine learning is crucial. AIOps can accurately detect the actual source which can help IT teams in performing efficient root cause analysis almost in real-time.

Threat detection & analysis

Along with anomaly detection, AIOps will play a critical role in enhancing the security of IT infrastructure. Security systems can use ML algorithms and AI’s self-learning capabilities to help the IT teams detect data breached and violations. By correlating various internal sources like log files, network and event logs, with the external information on malicious IPs and domains, AI can be used to detect anomalies and risk events through analysis. Advanced machine learning algorithms can be used to identify unexpected and potentially unauthorized and malicious activity within the infrastructure.

Although still early in deployment, companies are taking advantage of AI and machine learning to improve tech support and manage infrastructure.  AIOps, the convergence of AI and IT ops, will change the face of infrastructure management.

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What chatbots will do for your enterprise?

Gen X, Y or any other fancy term describing the current demographics is tuned to using voice, text and natural language to complete their work. That’s why a new generation of enterprise chatbots is needed at work.

Read over the textbook definition of a chatbot and you’ll understand it’s a computer program designed to hold conversations with humans over the internet. They can understand written and spoken text and interpret its meaning as well. The bot can then look up relevant information and deliver it to the user.

While chatbots reduces time and efforts, it’s not easy to create a chatbot that customers will trust. Businesses will have to consider the overall.

  • Security
  • Team complexity
  • Brand image
  • Scalability/availability
  • Identity and access management
  • Other parameters to fully integrate chatbots in their organizational structure

If correctly implemented enterprise chatbots can perform pre-defined roles and tasks to improve the business processes and activities.

Shortlisting the right chatbot

Automating repetitive and mundane work will increase the productivity, creativity, and efficiency of the organization. Evolution of chatbots will create more business opportunities for enterprises and new companies. Both SMBs and enterprises can improve their customer satisfaction with customized chatbots that help in offloading employee workload or support the various teams in the organization.

Enterprises first need to identify the type of chatbots needed for their organization to kick start their digital transformation. Depending on their requirements, there are two types of chatbots.

  • Standalone applications
  • Built within the messengers

Usually chatbots associated with messengers have an edge over standalone apps. They can be downloaded and used instantly. They are even easy to build and upgrade, faster compared to apps and websites and also cost effective. You also don’t have to worry about memory space.

AI based or machine learning chatbots learn over time from past questions and answers, and evolve their response accordingly.

What’s in it for enterprises?

There are some universal benefits that businesses in any industry or vertical can benefit from.

Streamlining your IT processes

A variety of business processes across your departments can be streamlined using chatbots. Your employees’ mundane, repetitive but essential tasks can be taken up by the chatbots, giving more time for revenue generating activities. For instance, they can be tasked with follow ups with clients or answering the FAQs by customers.

Act as personal assistants

Chatbots are a great help for the time constrained employees to manage, schedule, or cancel their meetings, setting alarms and other tasks. Context sensitive digital assistants help in organizing their daily routine by understanding the context, behaviors and patterns and suggesting recommendations.

24/7 customer support

Customer expectation is high with them demanding instant and quick resolution for their concerns and problems. Enterprise chatbot solutions offer a cost effective 24/7 customer services for you. Advancements in AI, machine learning and natural language processing (NLP) can allow them to understand the context, usage of slangs, and human conversation to a large extent. On a cautionary note, chatbots should easily handover the conversation to humans to avoid any unnecessary customer conflicts.

Generate business insights

The data deluge faced by the enterprises is costing them through lost insights and business opportunities. Vast data generated across the organization by employees, customers and business processes cannot be completely analyzed, and it leaves data gaps. Leveraging chatbots for processing and analyzing the stored data can result in identifying potential problem areas and take preemptive actions to mitigate the risks.

Reduce Opex & Capex costs

Enterprise chatbots are one-time investments, where you pay only for the chatbot, train it and its forever yours. No monthly payrolls, or sick leaves. You have a 24/7 virtual employee managing your routine and repetitive tasks.

Increase efficiency and productivity

The end result of all the above points is increased productivity. By training your employees about the services and products, a chatbot solution helps your employees to tackle the generic queries from customers. Thus, ending the time-consuming customer facing tasks and helping in the sales funnel.

In conclusion, chatbots are changing the working dynamics of enterprises. The best way to ensure a satisfied customer experience is to build bots that act without being supervised and offer the best solutions to their problems. With new advancements like AI, NLP and Machine Learning, it’s safe to say that chatbots are the future of enterprises.

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Can enterprises gain from cognitive automation?

What is cognitive automation (CA)?

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035,” stated Gray Scott. Cognitive automation is a subcategory of artificial intelligence (AI) technologies that imitates human behavior. Combined efforts of robotic process automation (RPA) and cognitive technologies such as natural language processing, image processing, pattern recognition and speech recognition has eased the automation process replacing humans. The best part of CA solutions are, they are pre-trained to automate certain business processes hence, they don’t need intervention of data scientists and specific models to operate on. Infact, a cognitive system can make more connection in a system without supervision using new structured and unstructured data.

Future of CA

There is a speedy evolution of CA with increasing investments in cognitive applications and software platforms. Market research indicates, approximately $2.5 billion has been invested in cognitive-related IT and business services. There is also an expectation of 70% rise in such investments by 2023. The focus areas where CA gained momentum are:

  • Quality checks and system recommendations
  • Diagnosis and treatment recommendations
  • Customer service automation
  • Automated threat detection and prevention
  • Fraud analysis and investigation

Difference between normal automation and CA

There is a basic difference between normal IT automation and CA technologies. Let’s try to understand it with a use case where a customer while filling an e-form to open an account in a bank, leaves few sections blank. A normal IT automation will detect it, flag it red and reject the form as incomplete. This then, will need human intervention to fix the issue. CA, in a similar situation, will auto-correct the issue without any human intervention. This will increase operational efficiency, reduce time and effort of the process and improve customer satisfaction.

Enterprises’ need for CA

As rightly mentioned by McKinsey, 45% of human intervention in IT enterprises can be replaced by automation. Tasks with high volumes of data requires more time to complete. CA can prove worthy in such situations and reshape processes in an efficient way. Businesses are becoming complex with time, and enterprises face a lot of challenges daily like; ensuring customer satisfaction, guaranteeing compliance, staying in competition, increasing efficiency and decision making. CA helps to take care of those challenges in an all-encompassing manner. CA can improve efficiency to the extent of 30 – 60% in email management and quote processing. It ensures an overall improvement in operational scalability, compliance and quality of business. It reduces TAT and error rates, thus impacting enterprises positively.

Benefits of CA in general

A collaboration between RPA and CA has multiplied the scope of enterprises to operate successfully and reap benefits to the extent that enterprises are able to achieve ROI of up to 300% in few months’ time, research reveals. The benefits enterprises can enjoy by adopting CA are:

  • It improves quality by reducing downtime and improving smart insights.
  • It improves work efficiency and enhances productivity with pattern identification and automation.
  • Cognitive computing and autonomous learning can reduce operational cost.
  • A faster processing speed can impact business performance and increases job satisfaction resulting employee retention, since it boosts employee satisfaction and engagement.
  • It increases business agility and innovation with provisioning of automation.
  • As a part of CA, Natural Language Processor (NLP) is a tool used in cognitive computing. It has the capacity to communicate more effectively and resolve critical incidents. This increases customer satisfaction to a great extent.

Enterprises using CA for their benefit:

  1. A leading IT giant combined cloud automation service with cognition to reduce 50% of server downtime in last two years. It also reduced TAT through auto resolution of more than 1500 server tickets every month. There was reduction of critical incidents by 89% within six months of cognitive collaboration.
  2. An American technology giant introduced a virtual assistant as one of their cognitive tools. It could understand twenty-two languages and could handle service requests without human intervention. It eased the process of examining insurance policies for clients, help customers open bank accounts, help employees learn company policies and guidelines.
  3. A leading train service in UK used virtual assistant starting from refund process to handling their customer queries and complaints.
  4. A software company in USA uses cognitive computing technology to provide real-time investment recommendations.
  5. Cognitive computing technology used in media and entertainment industries can extract information related to user’s age, gender, company logo, certain personalities and locate profile and additional information using Media Asset Management Systems. This helps in answering queries, adding a hint of emotion and understanding while dealing with a customer.

Conclusion

Secondary research reveals that the Cognitive Robotic Process Automation (CRPA) market will witness a CAGR of 60.9% during 2017 – 2026. The impact CA has on enterprises is remarkable and it is an important step towards the cognitive journey. CA can continuously learn and initiate optimization in a managed, secured and reliable way to leverage operational data and fetch actionable insights. Hence, we can conclude that enterprises are best poised to gain considerably from cognitive automation.

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8 Ways AI Will Impact Healthcare

Artificial Intelligence (AI) is still a layered subject that’s both exciting and scary to say the least. Given the new information being discovered each day, people are still nervous when it comes to letting AI handle their personal data (fears of security, privacy issues etc.). But they are comfortable with doctors and physicians using AI in healthcare for providing accurate and precise medical treatments and information.

This implies a growing acceptance of the impersonal AI in healthcare, where the physical and personal contact between the caregivers and patients is high. The myriad and increasingly mainstream applications of AI in healthcare are propelling this strong and growing acceptance.

Such openness to AI is vital for healthcare companies, as it empowers the patients and caregivers to gain valuable insights from the data collected and act on them accordingly. AI can analyze loads of medical data and identify patterns to detect any deviations in the individual patient’s behavior and suggest treatment plans / changes. It can sort through assist doctors to improve the accuracy of diagnosis and help in correct treatment.

This AI aided healthcare is not only beneficial to the patients, but also healthcare companies can save time and money performing basic, non-patient care activities (like writing chart notes and prescriptions, etc.) so that caregivers have more time to spend with people.

Research shows that amongst the largest sources of savings are robot-assisted surgery ($40 billion in savings), virtual nursing assistants ($20 billion) and administrative workflow assistance ($18 billion).

AI, Healthcare, and Interconnection.

The bridge between AI and healthcare can only function and give value if the interconnection is smooth and inter-operable. That’s because AI is highly data driven requiring a secure, instant, and low latency connectivity among the multitude data sources between the users and cloud applications.

Given the multi-tenant cloud architecture and the still existing traditional healthcare IT infrastructures, GAVS Technologies enables healthcare providers to easily migrate to the new AI enabled digital infrastructure.
Cost, transparency, and compliance with the various healthcare regulatory bodies are the biggest challenges today for healthcare institutions. With the GDPR already in effect, requiring data protection for all the collected data and its correct usage becoming mandatory, it’s vital for them to have a clear road map for their business strategies involving AI.

Here are eight ways that highlight the technologies and areas of the healthcare industry that are most likely to see a major impact from artificial intelligence.

• Brain-computer interfaces (BCI) backed by artificial intelligence can help restore the patients’ fundamental experiences of speech, movement and meaningful interaction with people and their environments, lost due to neurological diseases and trauma to the nervous system. BCI could drastically improve quality of life for patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.

• Artificial intelligence will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases. AI is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.

• AI could help mitigate the shortages of trained healthcare providers, including ultrasound technicians and radiologists which can significantly limit access to life-saving care in developing nations around the world. This severe deficit of qualified clinical staff can be overcome by AI taking over some of the diagnostic duties typically allocated to humans.

• Electronic Health Records (EHR) have played an instrumental role in the healthcare industry’s journey towards digitalization, but this has brought along with cognitive overload, endless documentation, and user burnout. EHR developers are now using AI to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time like clinical documentation, order entry, and sorting through their inbox mail.

• Smart devices using artificial intelligence to enhance the ability to identify patient deterioration or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties.

• Immunotherapy (using the body’s own immune system to attack malignancies) is one of best cancer treatments available now. But oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option. AI and Machine learning algorithms and its ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.

• AI to assimilate the health-related data generated through wearables and personal devices for better monitoring and extracting actionable insights from this large and varied data source.

• Using smartphones which have built-in AI software and hardware to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects is an important supplement to clinical quality imaging especially in under-served populations or developing nations where there is a shortage of specialists while reducing the time-to-diagnosis for certain complaints. Dermatology and ophthalmology are early beneficiaries of this trend.

• Leveraging AI for clinical decision support, risk scoring, and early alerting are some of the most promising areas of development for this revolutionary approach to data analysis.

• AI allow those in training to go through naturalistic simulations in a way that simple computer-driven algorithms cannot. The advent of natural speech and the ability of an AI computer to draw instantly on a large database of scenarios, means the response to questions, decisions or advice from a trainee can be challenging and the AI training programme can learn from previous responses from the trainee.

Contact GAVS Technologies to know more about how AI will impact Healthcare here at https://www.gavstech.com/reaching-us/

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