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.
This
article’s focus is on the Remediate function of ZIF. Most ITSM teams envision a
future of ticketless ITSM, driven by AI and Automation.
Remediate
being a key module ofZIF, has more than 500+ connectors to various ITSMtools,
Monitoring, Security and Incident management tools, storage/backup tools and
others.Few of the connectors are referenced below that enables quick automation
building.
Key Features of Remediate
Truly Agent-less software.
300+ readily available templates – intuitive workflow/activity-based tool for process automation from a rich repository of pre-coded activities/templates.
No coding or programming required to create/deploy automated workflows. Easy drag & drop to sequence activities for workflow design.
Workflow execution scheduling for pre-determined time or triggering from events/notifications via email or SMS alerts.
Can be installed on-premise or on the cloud, on physical or virtual servers
Self Service portal for end-users/admins/help-desk to handle tasks &remediation automatically
Fully automated service management life cycle from incident creation to resolution and automatic closure
Has integration packs for all leading ITSM tools
Key features for futuristic Automation Solutions
Although the
COVID pandemic has landed us in unprecedented times, we have been able to
continue supporting our customers and enabled their IT operations with ZIF
Remediate.
Self-learning capability to deliver Predictive/Prescriptive actionable alerts.
Access to multiple data sources and types – events, metrics, thresholds, logs, event triggers e.g. mail or SMS.
Support for a wide range of automation
Interactive Automation – Web, SMS, and email
Non-interactive automation – Silent based on events/trigger points
Supporting a wide range of advanced heuristics.
Benefits of AIOPS driven Automation
Faster
MTTR
Instant
identification of threats and appropriate responses
Faster
delivery of IT services
Quality
services leading to Employee and Customer satisfaction
Fulfillment
and Alignment of IT services to business performance
Interactive and Non-interactive automation
Through our automation journey so far, we have understood that the best automation empowers humans, rather than replacing them. By implementing ZIF Remediate, organizations can empower their people to focus their attention on critical thinking and value-added activities and let our platform handle mundane tasks by bringing data-driven insights for decision making.
Interactive Automation – Web portal, Chatbot and
SMS based
Non-interactive automations – Event or trigger
driven automation
Involved decision
driven Automations
ZIF Remediate has its unique, interactive automation capabilities, where many automation tools do not allow interactive decision making. Need approvals built into an automated change management process that involves sensitive aspects of your environment? Need numerous decision points that demand expert approval or oversight? We have the solution for you. Take an example of Phishing automation, here a domain or IP is blocked based on insights derived by mimicking an SOC engineer’s actions – parsing the observables i.e. URL, suspicious links or attachments in a phish mail and have those observables validated for threat against threat response tools, virus total, and others.
Some of the key benefits realized by our customers which include one of the largest manufacturing organizations, a financial services company, a large PR firm, health care organizations, and others.
Reduction of MTTR by 30% across various service requests.
Reduction of 40% of incidents/tickets, thus enabling productivity improvements.
Ticket triaging process automation resulting in a reduction of time taken by 50%.
Reclaiming TBs of storage space every week through snapshot monitoring and approval-driven model for a large virtualized environment.
Eliminating manual threat analysis by Phishing Automation, leading to man-hours being redirected towards more critical work.
Reduction of potential P1 outages by 40% through self-healing automations.
Alwin leads the Product Engineering for ZIF Remediate and zIrrus. He has over 20 years of IT experience spanning across Program & Portfolio Management for large customer accounts of various business verticals.
In his free time, Alwin loves going for long drives, travelling to scenic locales, doing social work and reading & meditating the Bible.
Architecture inspires people, no wonder so many famous writers, artists, politicians, and designers have such profound and fascinating observations about architecture. Whether embracing minimalism or adoring resplendence, everyone has experiences and tastes that shape the way they interact with the world. The Greek architectural beauties have captured the imagination of many. The crown jewel of their architecture is the “post and lintel” which was used for their grand, large, open-air structures that could accommodate 20,000 spectators.
Greeks are also famous for their Alphabets. When the Greek Architecture and Alphabets are merged, the state-of-the-art overarching “Big Data Processing Architecture” is produced; Lambda λ, kappa κ, and Zeta ζ.
Big Data Architectural patterns
The evolution of the technologies in Big Data in the last decade has presented a history of battles with growing data volume. An increasing number of systems are being built to handle the Volume, Velocity, Variety, Veracity, Validity, and Volatility of Big Data and help gain new insights and make better business decisions. A well-designed big data architecture must handle the 6 V’s of Big Data, save your company money, and help predict future trends.
Lambda (λ) Architecture
The Lambda Architecture λis an emerging paradigm in Big Data computing.
The name lambda architecture is derived from a functional point of view of data
processingi.e. all data processing is understood as the application of a
function to all data.
Lambda architecture is popular for its data processing technique of handling huge amounts of data by taking advantage of both a batch layer and a speed/stream-processing layer. This specific approach attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data. The outputs from both batch and speed layers can be merged before the presentation.
The efficiency of this architecture becomes evident in the form of increased throughput, reduced latency, and negligible errors, thus resulting in a linearly scalable architecture that scales out rather than scaling up.
Basic tenets of Lambda Architecture
The Lambda Architecture achieves high scalability and low
latency due to the following principles,
Immutability of Data
Data Denormalization
Precomputed Views
Immutability of Data
The Big Data immutability is based on similar principles as the immutability in programming data structures. The goal being the same – do not change the data in-place and instead create a new one. The data can’t be altered and deleted. This rule can be defined for eternity or for a specified time period.
Immutable data is fundamentally simpler than mutable data. The idea here is not to change the data in-place i.e. no updating or deleting of records but creating new ones. Now, this could be time-bound or for the eternity. Thus, write operations only add new data units. In CRUD parlance only CR (Create & Read) and no UD (Update & Delete).
This approach makes data handling highly scalable because it is very easy to distribute and replicate data. This immutable model makes the data aggregation kind of a logging system. With the attributes like “data creation timestamp”, the old and the most recent version can be distinguished. Apache Kafka – an append-only distributed log system is a great example of an immutable data store.
As a drawback, even more, data is generated, and answering queries becomes more difficult. For example, to find the current owner of a brand, the owner for that brand with the latest timestamp must be found.
In the mutable data model, it is no longer possible to find out that the brand Jaguar was once owned by Ford. This is different when using an immutable data model which is achieved by adding a timestamp to each data record.
Now it is possible to get both bits of information: the fact that Jaguar is now owned by Tata Motors (latest timestamp) and the fact it was formerly owned by Ford. It is also much easier to recover from errors because the old information is not deleted.
Data Denormalization
The traditional database systems are named for their storage efficiency and data integrity. It is possible due to the Normalization process like 1NF, 2NF, 3NF, BCNF, 4NF, and 5NF. Due to efficient normalization strategy, data redundancy is eliminated. The same data need not be saved in multiple places (tables) and any updates (partial or full) on the same, need not be done at multiple places (tables). But this makes the traditional databases poor at scaling their read performance since data from multiple places (tables) need to be brought together by complex and costly join operations.
For the sake of performance, Big data systems accept denormalization and duplication of data as a fact of life with the data schema such that data stored in-representation is equivalent to that after performing joins on normalized tables.
In this way, the knowledge about the schema is not necessary, and joins can be avoided, and the query results are faster. This also motivates the query-driven data modeling methodology. Albeit the data exists in multiple places after denormalization, the consistency of the data is ensured via strong consistency, timeline consistency, and eventual consistency models in the event of partial or full updates. This is often acceptable, especially when denormalized representations are used as precomputed views.
Precomputed Views
To give fast and consistent answers to queries on huge amounts of data, precomputed views are prepared both in the batch layer and in the speed layer. In the batch layer, these are constructed by applying a batch function to all the data. This leads to a transformation of the data into a more compact form suitable for answering a pre-defined set of queries. This idea is essentially the same as what is done in data warehousing.
Layers of Lambda
The Lambda Architecture solves the problem of computing
arbitrary functions on arbitrary data in real-time by decomposing the problem
into three layers,
Batch Layer or Cold Path
Speed Layer or Hot path
Serving Layer
Batch layer or Cold path
The nub of the λ is the master dataset. The master dataset is the source of truth in Lambda Architecture. The Master dataset must hold the following three properties,
Data is raw.
Data is immutable.
Data is eternally true.
This gives the Lambda architecture ability to reconstruct the application from the master data even if the whole serving layer data set is lost. The batch layer pre-computes results using a distributed processing system that can handle very large quantities of data. The batch layer aims at perfect accuracy by being able to process all available data when generating views.
The batch layer prefers re-computation
algorithms over incremental algorithms. The problem with incremental algorithms
is the failure to address the challenges faced by human mistakes. The
re-computational nature of the batch layer creates simple batch views as the
complexity is addressed during precomputation. Additionally, the responsibility
of the batch layer is to historically process the data with high accuracy.
Machine learning algorithms take time to train the model and give better
results over time. Such naturally exhaustive and time-consuming tasks are
processed inside the batch layer.
The problem with the batch layer is high latency. The batch
jobs must be run over the entire master dataset. These jobs can process data
that can be relatively old as they cannot keep up with the inflow of stream
data. This is a serious limitation for real-time data processing. To overcome
this limitation, the speed layer is very significant.
Frameworks and solutions such as Hadoop MapReduce, Spark core, Spark SQL, GraphX, and MLLib are the widely adopted big-data tools using batch mode. Batch schedulers include Apache Oozie, Spring Batch, and Unix crontab which, invoke the processing at a periodic interval.
Speed layer or Streaming layer or Hot path
The real-time data processing is realized in the speed
layer. The speed layer achieves up-to-date query results and compensates for
the high latency of the batch layer.
To create real-time views of the most recent data, this layer sacrifices throughput and decreases latency substantially. The real-time views are generated immediately after the data is received but are not as complete or precise as the batch layer. In contrast to the re-computation approach of the batch layer, the speed layer adopts incremental computational algorithms. Since the data is not complete i.e less data so less computation. The incremental computation is more complex, but the data handled in the speed layer is vastly smaller and the views are transient.
Most operations on streams are windowed operations operating on slices of time such as moving averages for the stock process every hour, top products sold this week, fraud attempts in banking, etc. Popular choices for stream-processing tools include Apache Kafka, Apache Flume, Apache Storm, Spark Streaming, Apache Flink, Amazon Kinesis, etc.
Serving Layer
The output from both the batch and speed layers are stored in the serving layer.pre-computed batch views are indexed in this layer for faster retrieval. All the on-demand queries from the reporting or presentation layer are served by merging the batch and real-time views and outputs a result.
The batch views for a serving layer are
produced from scratch. When a new version of a view becomes available, it must
be possible to completely swap out the older version with the updated view.
Scalable
A serving layer database must be capable of
handling views of arbitrary size. As with the distributed filesystems and batch
computation framework previously discussed, this requires it to be distributed
across multiple machines.
Random
reads
A serving layer database must support
random reads, with indexes providing direct access to small portions of the
view. This requirement is necessary to have low latency on queries.
Fault-tolerant
Because a serving layer database is
distributed, it must be tolerant of machine failures.
This is how Lambda Architecture λ handles humongous amounts of data with low latency queries in a fault-tolerant manner. Let’s see the various implementation of lambda architecture and its applications in the next part.
To be continued…
About the Author:
Bargunan is a Big Data Engineer and a programming enthusiast. His passion is to share his knowledge by writing his experiences about them. He believes “Gaining knowledge is the first step to wisdom and sharing it is the first step to humanity.”
Artificial Intelligence for IT operations or AIOps has exploded over the past few years. As more and more enterprises set about their digital transformation journeys, AIOps becomes imperative to keep their businesses running smoothly.
AIOps uses several technologies like
Machine Learning and Big Data to automate the identification and resolution of
common Information Technology (IT) problems. The systems, services, and
applications in a large enterprise produce volumes of log and performance data.
AIOps uses this data to monitor the assets and gain visibility into the
behaviour and dependencies among these assets.
According to a Gartner publication,
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.
Let us understand more about thePredict module
of ZIF.
Predictive Analytics is one of the main USP of
the ZIF platform. ZIF encompassesSupervised, Unsupervised and Reinforcement
Learning algorithms for realization of various business use cases (as shown
below).
How does the Predict Module of ZIF work?
Through its data ingestion capabilities, the ZIF
platform can receive and process all types of data (both structured and
unstructured) from various tools in the enterprise. The types of data can be
related to alerts, events, logs, performance of devices, relations of devices,
workload topologies, network topologies etc. By analyzing all these data, the platform
predicts the anomalies that can occur in the environment. These anomalies get
presented as ‘Opportunity Cards’ so that suitable action can be taken ahead
of time to eliminate any undesired incidents from occurring. Since this is ‘Proactive’
and not ‘Reactive’, it brings about a paradigm shift to any organization’s
endeavour to achieve 100% availability of their enterprise systems and
platforms. Predictions are done at multiple levels – application level,
business process level, device level etc.
Sub-functions of Prediction Module
How does the Predict module manifest to enterprise users of the platform?
Predict module categorizes the opportunity
cards into three swim lanes.
Warning
swim lane – Opportunity Cards that have an “Expected
Time of Impact” (ETI) beyond 60 minutes.
Critical
swim lane – Opportunity Cards that have an ETI within
60 minutes.
Processed
/ Lost– Opportunity Cards that have been processed
or lost without taking any action.
Few of the enterprises that realized the power of ZIF’s Prediction Module
A manufacturing giant in the US
A large non-profit mental
health and social service provider in New York
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.
Reinforcement learning (RL) is an area of Machine Learning (ML) that takes suitable actions to maximize rewards situations. The goal of reinforcement learning algorithms is to find the best possible action to take in a specific situation. Just like the human brain, it is rewarded for good choices and penalized for bad choices and learns from each choice. RL tries to mimic the way that humans learn new things, not from a teacher but via interaction with the environment. At the end, the RL learns to achieve a goal in an uncertain, potentially complex environment.
Understanding Reinforcement Learning
How does one learn cycling? How does a baby learn to walk? How do we become better at doing something with more practice? Let us explore learning to cycle to illustrate the idea behind RL.
Did somebody tell you how to cycle or gave you steps to follow? Or did you learn it by spending hours watching videos of people cycling? All these will surely give you an idea about cycling; but will it be enough to actually get you cycling? The answer is no. You learn to cycle only by cycling (action). Through trials and errors (practice), and going through all the positive experiences (positive reward) and negative experiences (negative rewards or punishments), before getting your balance and control right (maximum reward or best outcome). This analogy of how our brain learns cycling applies to reinforcement learning. Through trials, errors, and rewards, it finds the best course of action.
Components of Reinforcement Learning
The major components of RL are as detailed below:
Agent: Agent is the part of RL which takes actions, receives rewards for actions and gets a new environment state as a result of the action taken. In the cycling analogy, the agent is a human brain that decides what action to take and gets rewarded (falling is negative and riding is positive).
Environment: The environment represents the outside world (only relevant part of the world which the agent needs to know about to take actions) that interacts with agents. In the cycling analogy, the environment is the cycling track and the objects as seen by the rider.
State: State is the condition or position in which the agent is currently exhibiting or residing. In the cycling analogy, it will be the speed of cycle, tilting of the handle, tilting of the cycle, etc.
Action: What the agent does while interacting with the environment is referred to as action. In the cycling analogy, it will be to peddle harder (if the decision is to increase speed), apply brakes (if the decision is to reduce speed), tilt handle, tilt body, etc.
Rewards: Reward is an indicator to the agent on how good or bad the action taken was. In the cycling analogy, it can be +1 for not falling, -10 for hitting obstacles and -100 for falling, the reward for outcomes (+1, -10, -100) are defined while building the RL agent. Since the agent wants to maximize rewards, it avoids hitting and always tries to avoid falling.
Characteristics of Reinforcement Learning
Instead of simply scanning the datasets to find a mathematical equation that can reproduce historical outcomes like other Machine Learning techniques, reinforcement learning is focused on discovering the optimal actions that will lead to the desired outcome.
There are no supervisors to guide the model on how well it is doing. The RL agent gets a scalar reward and tries to figure out how good the action was.
Feedback is delayed. The agent gets an instant reward for action, however, the long-term effect of an action is known only later. Just like a move in chess may seem good at the time it is made, but may turn out to be a bad long term move as the game progress.
Time matters (sequential). People who are familiar with supervised and unsupervised learning will know that the sequence in which data is used for training does not matter for the outcome. However, for RL, since action and reward at current state influence future state and action, the time and sequence of data matters.
Action affects subsequent data RL agent receives.
Why Reinforcement Learning
The type of problems that reinforcement learning solves are simply beyond human capabilities. They are even beyond the solving capabilities of ML techniques. Besides, RL eliminates the need for data to learn, as the agent learns by interacting with the environment. This is a great advantage to solve problems where data availability or data collection is an issue.
Reinforcement Learning applications
RL is the darling of ML researchers now. It is advancing with incredible pace, to solve business and industrial problems and garnering a lot of attention due to its potential. Going forward, RL will be core to organizations’ AI strategies.
Reinforcement Learning at GAVS
Reinforcement Learning is core to GAVS’ AI strategy and is being actively pursued to power the IP led AIOps platform – Zero Incident FrameworkTM (ZIF). We had our first success on RL; developing an RL agent for automated log rotation in servers.
References:
Reinforcement Learning: An Introduction second edition by Richard S. Sutton and Andrew G. Barto
Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.
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
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.
Chatbots can be loosely defined as software to simulate human conversation. They are widely used as textbots or voicebots in social media, in websites to provide the initial engagement with visitors, as part of customer service/IT operations teams to provide tier 1 support round the clock and for various other organizational needs, as we’ll see later in the blog, in integration with enterprise tools/systems. Their prevalence can be attributed to how easy it has now become to get a basic chatbot up & running quickly, using the intuitive drag-drop interfaces of chatbot build tools. There are also many cloud-based free or low-cost AI platforms for building bots using the provided APIs. Most of these platforms also come with industry-specific content, add-on tools for analytics and more.
Rule-based chatbots can hold basic conversation with scripted ‘if/then’ responses for commonly raised issues/faqs, and redirect appropriately for queries beyond their scope. They use keyword matches to get relevant information from their datastore. Culturally, as we begin to accept and trust bots to solve problems and extend support; with companies beginning to see value in these digital resources; and with heavy investments in AI technologies, chatbots are gaining traction, and becoming more sophisticated. AI-led chatbots are way more complex than their rule-based counterparts and provide dynamically tailored, contextual responses based on the conversation and interaction history. Natural Language Processing capabilities give these chatbots the human-like skill to comprehend nuances of language and gauge the intent behind what is explicitly stated.
The Artificial Neural Network(ANN) for Natural Language Processing(NLP)
An ANN is an attempt at a tech equivalent of the human brain! You can find our blog on ANNs and Deep Learning here.
Traditional AI models are incapable of handling highly cognitive tasks like image recognition, image classification, natural language processing, speech recognition, text-speech conversion, tone analysis and the like. There has been a lot of success with Deep Learning approaches for such cerebral use cases. For NLP, handling the inherent complexities of language such as sentiment, ambiguity or insinuation, necessitates deeper networks and a lot of training with enormous amounts of data. Each computational layer of the network progressively extracts finer and more abstract details from the inputs, essentially adding value to the learnings from the previous layers. With each training iteration, the network adapts, auto-corrects and finetunes its weights using optimization algorithms, until it reaches a maturity level where it is almost always correct in spite of input vagaries. The USP of a deep network is that, armed with this knowledge gained from training, it is able to extract correlations & meaning from even unlabeled and unstructured data.
Different types of neural networks are particularly suited for different use cases. Recurrent Neural Networks(RNNs) are good for sequential data like text documents, audio and natural language. RNNs have a feedback mechanism where each neuron’s output is fed back as weighted input, along with other inputs. This gives them ‘memory’ implying they remember their earlier inputs, but with time the inputs get diluted by the presence of new data. A variant of the RNN helps solve this problem. Long Short Term Memory (LSTM) models have neurons(nodes) with gated cells that can regulate whether to ‘remember’ or ‘forget’ their previous inputs, thereby giving more control over what needs to be remembered for a long time versus what can be forgotten. For e.g.: it would help to ‘remember’ when parsing through a text document because the words and sentences are most likely related, but ‘forgetting’ would be better during the move from one text document to the next, since they are most likely unrelated.
The Chatbot Evolution
In the 2019 Gartner CIO Survey, CIOs identified chatbots as the main AI-based application used in their enterprises. “There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years”, says Van Baker, VP Analyst at Gartner.
Personal & Business communication morphs into the quickest, easiest and most convenient mode of the time. From handwritten letters to emails to phone calls to SMSs to mere status updates on social media is how we now choose to interact. Mr. Baker goes on to say that with the increase of millennials in the workplace, and their demand for instant, digital connections, they will have a large impact on how quickly organizations adopt the technology.
Due to these evolutionary trends, more organizations than we think, have taken a leap of faith and added these bots to their workforce. It is actually quite interesting to see how chatbots are being put to innovative use, either stand-alone or integrated with other enterprise systems.
Chatbots in the Enterprise
Customer service & IT service management(ITSM) are use cases through which chatbots gained entry into the enterprise. Proactive personalized user engagement, consistency and ease of interaction, round-the-clock availability & timely address of issues have lent themselves to operational efficiency, cost effectiveness and enhanced user experience. Chatbots integrated into ITSM help streamline service, automate workflow management, reduce MTTR, and provide always-on services. They also make it easier to scale during peak usage times since they reduce the need for customers to speak with human staff, and the need to augment human resources to handle the extra load. ChatOps is the use of chatbots within a group collaboration tool where they run between the tool and the user’s applications and automate tasks like providing relevant data/reports, scheduling meetings, emailing, and ease the collaborative process between siloed teams and processes, like in a DevOps environment where they double up as the monitoring and diagnostic tool for the IT landscape.
In E-commerce, chatbots can boost sales by taking the customer through a linear shopping experience from item search through purchase. The bot can make purchase suggestions based on customer preferences gleaned from product search patterns and order history.
In Healthcare, they seamlessly connect healthcare providers, consumers and information and ease access to each other. These bot assistants come in different forms catering to specific needs like personal health coach, companion bot to provide the much-needed conversational support for patients with Alzheimer’s, confidant and therapist for those suffering from depression, symptom-checker to provide initial diagnosis based on symptoms and enable remote text or video consultation with a doctor as required and so on.
Analytics provide insights but often not fast enough for the CXO. Decision-making becomes quicker when executives can query a chatbot to get answers, rather than drilling through a dashboard. Imagine getting immediate responses to requests like Which region in the US has had the most sales during Thanksgiving? Send out a congratulatory note to the leadership in that region. Which region has had the poorest sales? Schedule a meeting with the team there. Email me other related reports of this region. As can be seen here, chatbots work in tandem with other enterprise tools like analytics tools, calendar and email to make such fascinating forays possible.
Chatbots can handle the mundane tasks of Employee Onboarding, such as verification of mandatory documents, getting required forms filled, directing them to online new-hire training and ensuring completion.
When integrated with IoT devices, they can help in Inventory Management by sending out notifications when it’s time to restock a product, tracking shipment of new orders and alerting on arrival.
Chatbots can offer Financial Advice by recommending investment options based on transactional history, current investments or amounts idling in savings accounts, alerting customer to market impact on current portfolio and so much more.
As is evident now, the possibilities of such domain-specific chatbots are endless, and what we have seen is just a sampling of their use cases!
Choosing the Right Solution
The chatbot vendor market is crowded, making it hard for buyers to fathom where to even begin. The first step is an in-depth evaluation of the company’s unique needs, constraints, main use cases and enterprise readiness. The next big step is to decide between off-the shelf or in-house solutions. An in-house build will be an exact fit to needs, but it might be difficult to get long-term management buy-in to invest in related AI technologies, compute power, storage, ongoing maintenance and a capable data science team. Off-the-shelf solutions need a lot of scrutiny to gauge if the providers are specialists who can deliver enterprise-grade chatbots. Some important considerations:
The solution should (be);
Platform & Device Agnostic so it can be built once and deployed anywhere
Have good Integration Capabilities with tools, applications and systems in the enterprise
Robust with solid security and compliance features
Versatile to handle varied use cases
Adaptable to support future scaling
Extensible to enable additional capabilities as the solution matures, and to leverage innovation to provide advanced features such as multi-language support, face recognition, integration with VR, Blockchains, IoT devices
Have a Personality! Bots with a personality add a human-touch that can be quite a differentiator. Incorporation of soft features such as natural conversational style, tone, emotion, and a dash of humor can give an edge over the competition.
About the Author:
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!
(In the context of users of Applications (desktop, web or cloud-based), Services, Servers and components of IT environment, directly or indirectly.)
The question
may sound trivial, but it has a significant impact on the success of a company.
The user experience is a journey, from the time they use the application or
service, till after they complete the interaction. Experience can be determined
based on factors like Speed, Performance, Flawlessness, Ease of use, Security, Resolution
time, among others. Hence, monitoring the ‘Wow’ & ‘Woe’ moments of the users
is vital.
Monitor is a component of GAVS’ AIOps Platform, Zero Incident FrameworkTM (ZIF). One of the key objectives of the Monitor platform is to measure and improve end-user experience. This component monitors all the layers (includes but not limited to application, database, server, APIs, end-points, and network devices) in real-time that are involved in the user experience. Ultimately,this helps to drive the environment towards Zero Incidents.
This figure shows the capability of
ZIF monitoring that cut across all layers starting from end-user to storage and
how it is linked to other the components of the platform
Key Features
of ZIF Monitor are,
Unified solution for all IT
environment monitoring needs: The platform covers the end-to-end monitoring of an IT
landscape. The key focus is to ensure all verticals of IT are brought under
thorough monitoring. The deeper the monitoring, the closer an organization is
to attaining a Zero Incident EnterpriseTM.
Agents with self-intelligence: The intelligent agents capture
various health parameters about the environment. When the target environment is
already running under low resource, the agent will not task it with more load.
It will collect the health-related metrics and communicate through the
telemetry channel efficiently and effectively. The intelligence is applied in
terms of parameters to be collected, the period of collection and many more.
Depth of monitoring: The core strength of Monitor is it
comes with a list of performance counters which are defined by SMEs across all
layers of the IT environment. This is a key differentiator; the monitoring
parameters can be dynamically configured for the target environment. Parameters
can be added or removed on a need basis.
Agent & Agentless (Remote): The customers can choose from Agent
& Agentless options for the solutions. The remote solution is called as
Centralized Remote Monitoring Solution (CRMS). Each monitoring parameter can be
remotely controlled and defined from the CRMS. Even the agents that are running
in the target environment can be controlled from the server console.
Compliance: Plays a key role in terms of the
compliance of the environment. Compliance ranges from ensuring the availability
of necessary services and processes in the target environment and defines the
standard of what Application, Make, Version, Provider, Size, etc. that are
allowed in the target environment.
Auto discovery: Monitor can auto-discover the newer
elements (servers, endpoints, databases, devices, etc.) that are getting added
to the environment. It can automatically add those newer elements into the
purview of monitoring.
Auto scale: Centralized Remote Monitoring
Solution (CRMS) can auto-scale on its own when newer elements are added for
monitoring through auto-discovery. The auto scale includes various aspects,
like load on channel, load on individual polling engine, and load on each
agentless solution.
Real time user & Synthetic
Monitoring: Real-time
user monitoring is to monitor the environment when the user is active.
Synthetic monitoring is through simulated techniques. It doesn’t wait for the
user to make a transaction or use the system. Instead, it simulates the
scenario and provide insights to make decision proactively.
Availability & status of devices
connected: Monitor
also includes the monitoring of availability and control of USB and COM port
devices that are connected.
Black box monitoring:
It is not always possible to
instrument the application to get insights.Hence, the Black Box technique is
used. Here the application is treated as a black box and it is monitored in
terms of its interaction with the Kernel & OS through performance counters.
High level overview
of Monitor’s components,
Agents, Agentless: These are the means through which
monitoring is done at the target environment, like user devices, servers,
network devices, load balancers, virtualized environment, API layers, databases,
replications, storage devices, etc.
ZIF Telemetry Channel: The performance telemetry that are
collected from source to target are passed through this channel to the big data
platform.
Telemetry Data: Refers to the performance data and
other metrics collected from all over the environment.
Telemetry Database:This is the big data platform, in
which the telemetry data from all sources are captured and stored.
Intelligence Engine: This parses the telemetry data in
near real time and raises notifications based on rule-based threshold and as
well as through dynamic threshold.
Dashboard&Alerting Mechanism: These are the means through which the
results of monitoring are conveyed as metrics in dashboard and as well as notifications.
Integration with Analyze, Predict
& Remediate components: Monitoring module communicates the telemetry to Analyze & Predict components
of the ZIF platform for it to use the data for analysis and apply Machine Learning for
prediction. Both Monitor & Predict components, communicate with Remediate
platform to trigger remediation.
The Monitor component
works in tandem with Analyze, Predict and Remediate components of the ZIF
platform to achieve an incident free IT environment. Implementation of ZIF is
the right step to driving an enterprise towards Zero Incidents. ZIF is the only
platform in the industry which comes from the single product platform owner who
owns the end-to-end IP of the solution with products developed from scratch.
For more detailed information on GAVS’ Monitor, or to request a demo please visit zif.ai/products/monitor/
(To be continued…)
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.
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 Vendorsmatrix. 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.
IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.
As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.
The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.
Why AI in IT operations?
The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.
Visibility
The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.
The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.
Monitoring
When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.
The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.
Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).
Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.
Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).
Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.
Advanced Analytics
Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.
Remediation
AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.
Conclusion
The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.
GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.
IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.
As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.
The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.
Why AI in IT operations?
The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.
Visibility
The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.
The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.
Monitoring
When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.
The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.
Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).
Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.
Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).
Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.
Advanced Analytics
Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.
Remediation
AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.
Conclusion
The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.
GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.
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