Why is AIOps an Industrial Benchmark for Organizations to Scale in this Economy?

Business Environment Overview

In this pandemic economy, the topmost priorities for most companies are to make sure the operations costs and business processes are optimized and streamlined. Organizations must be more proactive than ever and identify gaps that need to be acted upon at the earliest.

The industry has been striving towards efficiency and effectivity in its operations day in and day out. As a reliability check to ensure operational standards, many organizations consider the following levers:

  1. High Application Availability & Reliability
  2. Optimized Performance Tuning & Monitoring
  3. Operational gains & Cost Optimization
  4. Generation of Actionable Insights for Efficiency
  5. Workforce Productivity Improvement

Organizations that have prioritized the above levers in their daily operations require dedicated teams to analyze different silos and implement solutions that provide the result. Running projects of this complexity affects the scalability and monitoring of these systems. This is where AIOps platforms come in to provide customized solutions for the growing needs of all organizations, regardless of the size.

Deep Dive into AIOps

Artificial Intelligence for IT Operations (AIOps) is a platform that provides multilayers of functionalities that leverage machine learning and analytics.  Gartner defines AIOps as a combination of big data and machine learning functionalities that empower IT functions, enabling scalability and robustness of its entire ecosystem.

These systems transform the existing landscape to analyze and correlate historical and real-time data to provide actionable intelligence in an automated fashion.

AIOps platforms are designed to handle large volumes of data. The tools offer various data collection methods, integration of multiple data sources, and generate visual analytical intelligence. These tools are centralized and flexible across directly and indirectly coupled IT operations for data insights.

The platform aims to bring an organization’s infrastructure monitoring, application performance monitoring, and IT systems management process under a single roof to enable big data analytics that give correlation and causality insights across all domains. These functionalities open different avenues for system engineers to proactively determine how to optimize application performance, quickly find the potential root causes, and design preventive steps to avoid issues from ever happening.

AIOps has transformed the culture of IT war rooms from reactive to proactive firefighting.

Industrial Inclination to Transformation

The pandemic economy has challenged the traditional way companies choose their transformational strategies. Machine learning powered automation for creating an autonomous IT environment is no longer a luxury. he usage of mathematical and logical algorithms to derive solutions and forecasts for issues have a direct correlation with the overall customer experience. In this pandemic economy, customer attrition has a serious impact on the annual recurring revenue. Hence, organizations must reposition their strategies to be more customer centric in everything they do. Thus, providing customers with the best-in-class service coupled with continuous availability and enhanced reliability has become an industry-standard.

As reliability and scalability are crucial factors for any company’s growth, cloud technologies have seen a growing demand. This shift of demand for cloud premises for core businesses has made AIOps platforms more accessible and easier to integrate. With the handshake between analytics and automation, AIOps has become a transformative technology investment that any organization can make.

As organizations scale in size, so does the workforce and the complexity of the processes. The increase in size often burdens organizations with time-pressed teams having high pressure on delivery and reactive housekeeping strategies. An organization must be ready to meet the present and future demands with systems and processes that scale seamlessly. This why AIOps platforms serve as a multilayered functional solution that integrates the existing systems to manage and automate tasks with efficiency and effectivity. When scaling results in process complexity, AIOps platforms convert the complexity to effort savings and productivity enhancements.

Across the industry, many organizations have implemented AIOps platforms as transformative solutions to help them embrace their present and future demand. Various studies have been conducted by different research groups that have quantified the effort savings and productivity improvements.

The AIOps Organizational Vision

As the digital transformation race has been in full throttle during the pandemic, AIOps platforms have also evolved. The industry did venture upon traditional event correlation and operations analytical tools that helped organizations reduce incidents and the overall MTTR. AIOps has been relatively new in the market as Gartner had coined the phrase in 2016.  Today, AIOps has attracted a lot of attention from multiple industries to analyze its feasibility of implementation and the return of investment from the overall transformation. Google trends show a significant increase in user search results for AIOps during the last couple of years.

ai automated root cause analysis solution

While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:

  1. Understanding the feasibility and concerns for its future adoption
  2. Classification of business processes and use cases for AIOps intervention
  3. Quantification of operational gains from incident management using the functional AIOps tools

AIOps is truly visioned to provide tools that transform system engineers to reliability engineers to bring a system that trends towards zero incidents.

Because above all, Zero is the New Normal.

About the Author –

Ashish Joseph

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs a series called #BizPective on LinkedIn and Instagram focusing on contemporary business trends from a different perspective. Outside work, he is very passionate about basketball, music and food.

Generative Adversarial Networks (GAN)

In my previous article (zif.ai/inverse-reinforcement-learning/), I had introduced Inverse Reinforcement Learning and explained how it differs from Reinforcement Learning. In this article, let’s explore Generative Adversarial Networks or GAN; both GAN and reinforcement learning help us understand how deep learning is trying to imitate human thinking.

With access to greater hardware power, Neural Networks have made great progress. We use them to recognize images and voice at levels comparable to humans sometimes with even better accuracy. Even with all of that we are very far from automating human tasks with machines because a tremendous amount of information is out there and to a large extent easily accessible in the digital world of bits. The tricky part is to develop models and algorithms that can analyze and understand this humongous amount of data.

GAN in a way comes close to achieving the above goal with what we call automation, we will see the use cases of GAN later in this article.

This technique is very new to the Machine Learning (ML) world. GAN is a deep learning, unsupervised machine learning technique proposed by Ian Goodfellow and few other researchers including Yoshua Bengio in 2014. One of the most prominent researcher in the deep learning area, Yann LeCun described it as “the most interesting idea in the last 10 years in Machine Learning”.

What is Generative Adversarial Network (GAN)?

A GAN is a machine learning model in which two neural networks compete to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.

The logic of GANs lie in the rivalry between the two Neural Nets. It mimics the idea of rivalry between a picture forger and an art detective who repeatedly try to outwit one another. Both networks are trained on the same data set.

A generative adversarial network (GAN) has two parts:

  • The generator (the artist) learns to generate plausible data. The generated instances become negative training examples for the discriminator.
  • The discriminator (the critic) learns to distinguish the generator’s fake data from real data. The discriminator penalizes the generator for producing implausible results.

GAN can be compared with Reinforcement Learning, where the generator is receiving a reward signal from the discriminator letting it know whether the generated data is accurate or not.

Generative Adversarial Networks

During training, the generator tries to become better at generating real looking images, while the discriminator trains to be better classify those images as fake. The process reaches equilibrium at a point when the discriminator can no longer distinguish real images from fakes.

Generative Adversarial Networks

Here are the steps a GAN takes:

  • The input to the generator is random numbers which returns an image.
  • The output image of the generator is fed as input to the discriminator along with a stream of images taken from the actual dataset.
  • Both real and fake images are given to the discriminator which returns probabilities, a number between 0 and 1, 1 meaning a prediction of authenticity and 0 meaning fake.

So, you have a double feedback loop in the architecture of GAN:

  • We have a feedback loop with the discriminator having ground truth of the images from actual training dataset
  • The generator is, in turn, in a feedback loop along with the discriminator.

Most GANs today are at least loosely based on the DCGAN architecture (Radford et al., 2015). DCGAN stands for “deep, convolution GAN.” Though GANs were both deep and convolutional prior to DCGANs, the name DCGAN is useful to refer to this specific style of architecture.

Applications of GAN

Now that we know what GAN is and how it works, it is time to dive into the interesting applications of GANs that are commonly used in the industry right now.

Generative Adversarial Networks

Can you guess what’s common among all the faces in this image?

None of these people are real! These faces were generated by GANs, exciting and at the same time scary, right? We will focus about the ethical application of the GAN in the article.

GANs for Image Editing

Using GANs, appearances can be drastically changed by reconstructing the images.

GANs for Security

GANs has been able to address the concern of ‘adversarial attacks’.

These adversarial attacks use a variety of techniques to fool deep learning architectures. Existing deep learning models are made more robust to these techniques by GANs by creating more such fake examples and training the model to identify them.

Generating Data with GANs

The availability of data in certain domains is a necessity, especially in domains where training data is needed to model learning algorithms. The healthcare industry comes to mind here. GANs shine again as they can be used to generate synthetic data for supervision.

GANs for 3D Object Generation

GANs are quite popular in the gaming industry. Game designers work countless hours recreating 3D avatars and backgrounds to give them a realistic feel. And, it certainly takes a lot of effort to create 3D models by imagination. With the incredible power of GANs, wherein they can be used to automate the entire process!

GANs are one of the few successful techniques in unsupervised machine learning and it is evolving quickly and improving our ability to perform generative tasks. Since most of the successful applications of GANs have been in the domain of computer vision, generative model sure has a lot of potential, but is not without some drawbacks.

About the Author –

Naresh B

Naresh is a part of Location Zero at GAVS as an AI/ML solutions developer. His focus is on solving problems leveraging AI/ML.
He strongly believes in making success as a habit rather than considering it as a destination.
In his free time, he likes to spend time with his pet dogs and likes sketching and gardening.

Data Migration Powered by RPA

What is RPA?

Robotic Process Automation(RPA) is the use of specialized software to automate repetitive tasks. Offloading mundane, tedious grunt work to the software robots frees up employee time to focus on more cerebral tasks with better value-add. So, organizations are looking at RPA as a digital workforce to augment their human resources. Since robots excel at rules-based, structured, high-volume tasks, they help improve business process efficiency, reduce time and operating costs due to the reliability, consistency & speed they bring to the table.

Generally, RPA is low-cost, has faster deployment cycles as compared to other solutions for streamlining business processes, and can be implemented easily. RPA can be thought of as the first step to more transformative automations. With RPA steadily gaining traction, Forrester predicts the RPA Market will reach $2.9 Billion by 2021.

Over the years, RPA has evolved from low-level automation tasks like screen scraping to more cognitive ones where the bots can recognize and process text/audio/video, self-learn and adapt to changes in their environment. Such Automation supercharged by AI is called Intelligent Process Automation.

Use Cases of RPA

Let’s look at a few areas where RPA has resulted in a significant uptick in productivity.

Service Desk – One of the biggest time-guzzlers of customer service teams is sifting through scores ofemails/phone calls/voice notes received every day. RPA can be effectively used to scour them, interpret content, classify/tag/reroute or escalate as appropriate, raise tickets in the logging system and even drive certain routine tasks like password resets to closure!

Claims Processing – This can be used across industries and result in tremendous time and cost savings.This would include interpreting information in the forms, verification of information, authentication of e-signatures & supporting documents, and first level approval/rejection based on the outcome of the verification process.

Data Transfers – RPA is an excellent fit for tasks involving data transfer, to either transfer data on paperto systems for digitization, or to transfer data between systems during data migration processes.

Fraud Detection – Can be a big value-add for banks, credit card/financial services companies as a first lineof defense, when used to monitor account or credit card activity and flag suspicious transactions.

Marketing Activities – Can be a very resourceful member of the marketing team, helping in all activities

right from lead gen, to nurturing leads through the funnel with relevant, personalized, targeted content



RPA can be used to generate reports and analytics on predefined parameters and KPIs, that can help

give insights into the health of the automated process and the effectiveness of the automation itself.

The above use cases are a sample list to highlight the breadth of their capabilities. Here are some industry-specific tasks where RPA can play a significant role.

Banks/Financial Services/Accounting Firms – Account management through its lifecycle, Cardactivation/de-activation, foreign exchange payments, general accounting, operational accounting, KYC digitization

Manufacturing, SCM –Vendor handling, Requisition to Purchase Order, Payment processing, Inventorymanagement

HR – Employee lifecycle management from On-boarding to Offboarding, Resume screening/matching

Data Migration Triggers & Challenges

A common trigger for data migration is when companies want to sunset their legacy systems or integrate them with their new-age applications. For some, there is a legal mandate to retain legacy data, as with patient records or financial information, in which case these organizations might want to move the data to a lower-cost or current platform and then decommission the old system.

This is easier said than done. The legacy systems might have their data in flat files or non-relational DBs or may not have APIs or other standards-based interfaces, making it very hard to access the data. Also, they might be based on old technology platforms that are no longer supported by the vendor. For the same reasons, finding resources with the skillset and expertise to navigate through these systems becomes a challenge.

Two other common triggers for data migrations are mergers/acquisitions which necessitate the merging of systems and data and secondly, digital transformation initiatives. When companies look to modernize their IT landscape, it becomes necessary to standardize applications and remove redundant ones across application silos. Consolidation will be required when there are multiple applications for the same use cases in the merged IT landscape.

Most times such data migrations can quickly spiral into unwieldy projects, due to the sheer number, size, and variety of the systems and data involved, demanding meticulous design and planning. The first step would be to convert all data to a common format before transition to the target system which would need detailed data mappings and data cleansing before and after conversion, making it extremely complex, resource-intensive and expensive.

RPA for Data Migration

Structured processes that can be precisely defined by rules is where RPA excels. So, if the data migration process has clear definitions for the source and target data formats, mappings, workflows, criteria for rollback/commit/exceptions, unit/integration test cases and reporting parameters, half the battle is won. At this point, the software bots can take over!

Another hurdle in humans performing such highly repetitive tasks is mental exhaustion, which can lead to slowing down, errors and inconsistency. Since RPA is unfazed by volume, complexity or monotony, it automatically translates to better process efficiency and cost benefits. Employee productivity also increases because they are not subjected to mind-numbing work and can focus on other interesting tasks on hand. Since the software bots can be configured to create logfiles/reports/dashboards in any format, level of detail & propagation type/frequency, traceability, compliance, and complete visibility into the process are additional happy outcomes!

To RPA or not to RPA?

Well, while RPA holds a lot of promise, there are some things to keep in mind

  • Important to choose the right processes/use-cases to automate, else it could lead to poor ROI
  • Quality of the automation depends heavily on diligent design and planning
  • Integration challenges with other automation tools in the landscape
  • Heightened data security and governance concerns since it will have full access to the data
  • Periodic reviews required to ensure expected RPA behavior
  • Dynamic scalability might be an issue when there are unforeseen spikes in data or usage patterns
  • Lack of flexibility to adapt to changes in underlying systems/platforms could make it unusable

But like all other transformational initiatives, the success of RPA depends on doing the homework right, taking informed decisions, choosing the right vendor(s) and product(s) that align with your Business imperatives, and above all, a whole-hearted buy-in from the business, IT & Security teams and the teams that will be impacted by the RPA.