Design Thinking 101

Vasudevan Gopalan

Is the end-user at the center of everything you do? Do you consider human emotions while conceptualizing a product or a solution? Well, let us open the doors of Design Thinking

What is Design Thinking?

  • Design thinking is both an ideology and a process, concerned with solving in a highly user-centric way.
  • With its human-centric approach, design thinking develops effective solutions based on people’s needs.
  • It has evolved from a range of fields – including architecture, engineering, business – and is also based on processes used by designers.
  • Design thinking is a holistic product design approach where every product touch point is an opportunity to delight and benefit our users.

Human Centred Design

With ‘thinking as a user’ as the methodology and ‘user satisfaction’ as the goal, design thinking practice supports innovation and successful product development in organizations. Ideally, this approach results in translating all the requirements into product features.

Part of the broader human centred design approach, design thinking is more than cross-functional; it is an interdisciplinary and empathetic understanding of our user’s needs. Design thinking sits right up there with Agile software development, business process management, and customer relationship management.

5 Stages of Design Thinking

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  • Empathize: This stage involves gathering insights about users and trying to understand their needs, desires, and objectives.
  • Define: This phase is all about identifying the challenge. What difficulties do users face? What are the biggest challenges? What do users really need?
  • Ideate: This step, as you may have already guessed, is dedicated to thinking about the way you can solve the problems you have identified with the help of your product. The product team, designers, and software engineers brainstorm and generate multiple ideas.
  • Prototype: The fourth stage brings you to turn your ideas into reality. By creating prototypes, you test your ideas’ fitness.
  • Test: You present the prototype to customers and find out if it solves their problem and provides users with what they need. Note that this is not the end of the journey; you need to get feedback from the users, adjust the product’s functionality, and test it again. This is a continuous process similar to the build-measure-learn approach in the lean start-up methodology.
Design Thinking

Benefits of Design Thinking in Software Development

1. Feasibility check: Design thinking enables software development companies to test the feasibility of the future product and its functionality at the initial stage. It enables them to keep end-user needs in mind, clearly specify all requirements and translate all this into product features.

2. No alarms and no surprises: Once you’ve tested your MVP and gathered feedback from users, the team can confidently proceed to the product development. You can be quite sure that there will be little to no difference between the approved concept and final version.

3. Clarity and transparency: Design thinking approach allow product designers/developers to broaden their vision, understand and empathise with the end-users’ problems and have a detailed blueprint of the solution they should eventually deliver.

4. Continuous improvement: The product can be (and sometimes should be) modified after its release when user feedback is at hand. It becomes clear which features work and which can be done away with. The product can undergo some series enhancements on the basis of feedback. This leaves place for continuous improvement and software development process becomes flexible and smooth.

Real-world Success Stories

1. PepsiCo

During Indra Nooyi’s term as PepsiCo’s CEO, the company’s sales grew 80%. It is believed that design thinking was at the core of her successful run. In her efforts to relook at the company’s innovation process and design experience, she asked her direct reportees to fill an album full of photos of what they considered represents good design. Uninspired by the result, she probed further to realize that it was imperative to hire a designer.

“It’s much more than packaging… We had to rethink the entire experience, from conception to what’s on the self to the post product experience.”, she told the Harvard Business Review.

While other companies were adding new flavours or buttons to their fountain machines, PepsiCo developed a touch screen fountain machine, a whole new interaction between humans and machines.

“Now, our teams are pushing design through the entire system, from product creation, to packaging and labelling, to how a product looks on the shelf, to how consumers interact with it,” she said.

2. Airbnb

Back in 2009, Airbnb’s revenue was limping. They realized that poor quality images of rental listings may have something to do with it. They flew some of their employees to a city and got them to take high quality photos and upload it on their website. This resulted in a 100% increase in their revenue.

Instead of focusing on scalability, the team turned inward and asked, ‘what does the customer need?’ This experiment taught them a few big lessons, empathy being just as important as code was one of them.

3. Mint.com

Mint.com is a web-based personal financial management website. Part of their success is attributed to the human-centric design of the website which tracks and visualizes how a person is spending their money. Bank accounts, investments, and credit cards can easily be synchronized on Mint, which then categorizes the expenses to help the user visualize their spending. They built a product that illustrates a core principle of design thinking: truly understanding the position and mindset of the user. They had 1.5 million customers within 2 years.

Design thinking is a human-centred approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.

References

https://www.researchgate.net/publication/226141981_Design_Thinking_A_Fruitful_Concept_for_IT_Development

https://blog.brainstation.io/how-5-ceos-used-design-thinking-to-transform-their-companies/

About the Author –

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.

The Pandemic and Social Media

Prabhakar Mandal

The COVID-19 outbreak has established the importance of digital readiness during pandemics. Building the necessary infrastructure to support a digitized world is the current mandate.

Technology has advanced much in the past century since we were hit by the Spanish Flu pandemic in 1918, and it plays a crucial role in keeping our society functional. From remote working to distance learning, and from telehealth to robot deliveries, our world is set to witness a lasting change post this pandemic.

As with other major and minor events of the past few years, social media is playing a big role in shaping people’s perception of the ongoing pandemic. Not just that, the social media platforms have also contributed to spreading information/misinformation, helping people cope with the strange times, and raising awareness about some pressing issues.

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Social Media and the pandemic: The Good!

Social media is one of the most effective ways to share news nowadays (it may be the only way for some people), especially if you are trying to alert the masses quickly. First-hand accounts of those who were infected and recovered were available almost in real-time. Scenes of lockdowns from the countries that first imposed it gave us a heads-up on what was due to come. If only we’d paid more heed to it.

With most of the world stuck at home, our mobile devices have increasingly become the go-to option to connect with the outside world. Social media usage has surged during the lockdown, with various apps witnessing a manifold increase in their traffic.

From educating to entertaining, social media platforms have stepped up as well. Movie and video streaming apps have redefined movie/video watching behavior by introducing features that allow users to host long-distance movie nights with friends and family.

We also witnessed a surge in various ‘online challenges’ that people could do in their homes and upload online. While some may view them as naïve, experts claim these are part of the various coping mechanisms for people.

Social media surfing has gained a significant share in the pie of leisure activities. Be honest, how many of us living alone are doing anything but scrolling these apps in our free time? But thanks to the social media ‘influencers’, scores of us are being motivated to workout at home, eat healthily, pick up a book, or learn something new.

Posts from health workers and others on the frontline have also helped spread the word on the difficulties they’re facing and rallied efforts to help them.

Online solidarity has spilled over offline as well. People are taking to social media to offer support in any way they can, such as picking up groceries for those who are unable to leave home or sharing information on how to support local businesses who are struggling. Communities are rallying together to support organizations and individuals by opening fundraisers to a larger audience.

Social Media and COVID-19: The Bad

Unfortunately, the impact of social media has not been all good. News on social media spreads fast, fake news even faster. Misinformation can cause panic, and can even turn out to be fatal on health issues. As a practice, we should all do a bit of research and validate the information from ‘reputed sources’ before sharing it.

This next bit is more of a tip…Whether it’s a business or a personal profile, you should refrain from posting anything that makes fun of, ridicules, or trivializes the situation. Not only is that insensitive, but it could also spell trouble for you, especially as a business.

The ‘influencers’ have been found guilty of misusing their power and taking advantage of the situation. Various inauthentic posts had gone viral before being pulled down. Do social validation and fame know no limits?

It is true that people often turn to social media as a stress-buster, but experts say it is equally stress-inducing for some individuals. It is important to note here that we’re also in the midst of an ‘infodemic’ – an anxiety-triggering over-abundance of information.

It is easy to overlook, especially now, the devastation that mental health issues cause globally. Studies have reported an increase in mental health issues attributed to social media in recent years. Psychologists say the lockdown will only add to that. Needless to say, mental health has a bearing on physical health as well.

Anti-rich sentiments have also gained momentum in the past weeks, as the pandemic makes the class divides glaringly obvious.

Conclusion

From the transparency that we have gained through this current COVID-19 situation, we now understand that we were not prepared to handle it. Many developed countries have had their health systems overwhelmed, those on the frontlines are being overworked and even the most advanced nations are stumbling to get their economies back up. The next pandemic is not a matter of “if it happens”, but “when it happens”.We need to be prepared at an individual and collective level. Indeed, technology has advanced and will continue to advance exponentially, but institutions and societies need to accelerate in adapting to it and continue investing in building the technology systems for the preparedness.

About the Author –

Prabhakar is a recruiter by profession and cricketer by passion. His focus is on hiring for the infra verticle. He hails from a small town in Bihar was brought up in Pondicherry. Prabhakar has represented Pondicherry in the U-19 cricket (National School Games). In his free time, he enjoys reading, working on his health and fitness, and spending time with his family and friends.

Cognitive Computing

Artificial Intelligence for IT Operations

Kalpana Vijayakumar

Is it possible for a computer to think and act without human intervention? The answer is yes, and that is called Cognitive computing.

Cognitive computing includes technology platforms that combine machine learning, reasoning, natural language processing, speech, vision, and human computer intervention that mimic the human brain, and solve problems without human assistance. Cognitive computing involves deep learning algorithms and big data analytics to provide insights.

The purpose of cognitive computing is to build a computing framework that can solve complicated problems without frequent human intervention. To implement it, cognitive computing consortium has recommended the following features.

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Scope of Cognitive Computing

We do have to accept that computers have been faster at calculations and processing than humans for decades. But, in a few cases, they have failed to accomplish the tasks that humans take for granted, like understanding the natural language and recognizing unique objects in the images and processing them. Cognitive computing solves all these challenges. They can act in complex situations and have a far-reaching impact on our lives.

Pera study by the IBM Institute for business value – cognitive computing involves three capabilities. These capabilities are related to the ways people think and demonstrate their cognitive abilities in their day-to-day life.

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The current cognitive computing landscape is dominated by large players – IBM, Microsoft, and Google. IBM being the pioneer of this technology has invested $26 bn dollars in big data and analytics, now spends close to one-third of its R&D budget in developing cognitive computing technology. IBM and Google have acquired some of their rivals and the market is moving towards consolidation. Below are the leading players in this market.

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IBM Watson

Watson assistant is IBM’s AI product that allows you to build, train, and deploy conversational simulators into any applications, device, or channel.

Most chatbots try to mimic human interactions, which can frustrate the end-user when a misunderstanding occurs. Watson Assistant aims to resolve that. It knows how to handle the end-user sensibly and when to direct queries to a human executive. It can be deployed on any cloud or on-premises environment.

Watson supercomputer processes at a rate of 80 teraflops (i.e. trillion floating-point operations per second).To replicate a high functioning human’s ability to answer questions, Watson accesses 90 servers with a combined data store of over 200 million pages of information, which it processes against six million logic rules.

Microsoft Cognitive Services

The machine-learned smarts that enable Microsoft’s Skype Translator, Bing and Cortana to accomplish tasks such as translating conversations, compiling knowledge and understanding the intent of spoken words are increasingly finding their way into third-party applications that people use every day. The democratization of AI is coming as part of Microsoft cognitive services, a collection of 25 tools that allows developers to add features such as emotions and sentiment, detection, vision and speech recognition and language understanding to their applications with zero experience in machine learning.

“Cognitive services is about taking all the machine learning algorithms and AI smarts that we have in this company and exposing them to developers through APIs so that they don’t have to invent the technology themselves”, Mike Seltzer, a principal researcher in the Speech and Dialog research group at Microsoft’s lab in Redmond, Washington.

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Google DeepMind

DeepMind was acquired by Google in 2014 and is considered to be a leading player in AI research. If you have used Google, there’s a high possibility that you’ve interacted with DeepMind in some way, as its deep learning tools have been implemented across the spectrum of Google products and services. Some of the most prominent uses for DeepMind AI includes speech recognition, image recognition, fraud detection, spam identification, handwriting recognition, translation, Google Maps Street View, and Local Search.

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Google devices like an Android Phone or Google Home, have invaded our homes and has impacted our lives. Every time you say, “Okay, Google” followed by a question, DeepMind helps Google Assistant understand what you are saying. Unlike Amazon’s Alexa, which uses eight microphones to understand voice commands, Google Home’s DeepMind-powered voice recognition system requires only two.

Cognitive Scale

Cognitive Scale founded by former members of the IBM Watson team provides cognitive cloud software for enterprises. Cognitive Scale’s augmented intelligence platform delivers insights-as-a-service and accelerates the creation of cognitive applications in healthcare, retail, travel, and financial services. They help businesses make sense from ‘dark data’ – messy, disparate, first and third-party data and drive actionable insights and continuous learning.

Spark Cognition Spark Cognition is an Austin-based start-up formed in 2014. Spark Cognition develops AI-Powered cyber-physical software for the safety, security, and reliability of IT, OT. The technology is more inclined towards manufacturing. It is capable of harnessing real-time sensor data and learning from it continuously, allowing for more accurate risk mitigation and prevention policies to intervene and avert disasters.

Cognitive Computing Use Cases

According to tech pundits, cognitive computing is the future. Many successful and established businesses have already integrated the technology into their business affairs. There are a number of successful use case scenarios and cognitive computing examples that show the world how to implement cognitive computing, efficiently. Let us look at some successful use cases of the technology.

Cora- Intelligent Agent by Royal Bank of Scotland

With the help of IBM Watson, Royal Bank of Scotland developed an intelligent assistant that is capable of handling 5000 queries in a single day. Using cognitive learning capabilities, the assistant gave RBS the ability to analyze customer grievance data and create a repository of commonly asked questions. Not only did the assistant analyze queries, but it was also capable of providing 1000 different responses and understand 200 customer intents. The digital assistant learned how customers ask general questions, how to handle the query, and transfer to a human agent if it is too complicated.

Healthcare Concierge by Welltok

Welltok developed an efficient healthcare concierge – CafeWell. It is a holistic population health tool that is being used by health insurance providers to help their customers with relevant information that improves their health. By collecting data from various sources and instant processing of questions by end-users, CafeWell offers smart and custom health recommendations that enhance the health quotient.

Personal Travel Planner by WayBlazer

Powered with cognitive technology, WayBlazer’s travel planner makes it easier for travelers to plan for trips by asking questions in natural language. The concierge asks basic questions and provides customized results by collecting and processing travel data as well as insights about traveler preferences.

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Such type of cognitive-powered tool helps travelers save time while searching for flights, booking hotels, and planning other activities. Travel agents have been successfully using such tools which have helped increase their revenues and customer delight at the same time.

Edge up’s Smart Tool to Manage Fantasy Football Teams via Mobile App

Fantasy Football is a very popular entertainment for more than 33 million people around the globe. With the help of cognitive learning and computing, Edge Up Sports developed a tool and integrated with their mobile app that helped users to draft their fantasy teams by asking simple questions.

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The questions, drafted in natural language, making it easier for users to take a decision which is then analyzed by the system by browsing through data about a player across social media, news reports, and gauging user sentiment that help team managers make better decisions.

Conclusion

Cognitive computing doesn’t bring a drastic novelty into the AI and big data industry. Rather, it urges digital solutions to meet human-centric requirements like act, think, and behave like a human in order to achieve maximum synergy from human-machine interaction. It is believed that soon every digital system will be measured based on its cognitive abilities.

Cognitive computing will be a significant step towards digital humanism!

References

About the Author

Kalpana is a database developer. She strongly believes that “It’s not that we use technology, we live technology.” Outside of her professional role, Kalpana is passionate about travelling and watching movies.

Dashboard SAP Lumira Designer – Write Back Functionality

Mohammed Fazal Uddin Kashif

Dashboards are considered to be one of the key success factors of businesses. Easy access to real-time KPIs allows them to be proactive and address business challenges before they impact the bottom line.

SAP Lumira Designer, formerly known as SAP Design Studio, is one of the robust products available in the market for developing top-of-the-line analytical applications and business dashboards. However, every tool by its core functionality has limitations for some use cases and business processes. One of such considerable cases is the ability to write back to a database directly from the dashboard.

Write back functionality assists business users to modify the data while analyzing from the dashboard rather than doing it in the source system. This functionality facilitates business users to manipulate the data and reflects refreshed data in the dashboard for further review and assessment.

This article introduces the use of Lumira SDK Extension component, POST RESPONSE PARSER, which enables the core range of Lumira designer to expand its boundary to include write-back functionality by integrating external Web API into the Lumira Dashboard.

Integrating Post Response Parser SDK Extension, the Lumira dashboard could be transformed from a pure data visualization application into an interactive data management analytical application.

Data Exploration & Smart Visualizations

Dashboards are analytical tools that visually track, analyzes and display Key Performance Indicators (KPIs) to the business processes or the portfolios. It provides a comprehensive snapshot of the performance of a key component within the portfolio. KPIs are business metrics which assists the leadership team to arrive at key decisions and drive towards the goals.

Business Dashboards and analytical applications provide at-a-glance visual and graphical representation of data which eliminates the need to go through long and complex excel spreadsheets.

Also, it’s time-consuming and difficult to pull out the most important business information whereas presenting that information in an appealing, visual way is more result-driven and effective.

Interactive dashboards enable us to visualize the data, filter on demand and simply click to dive deeper, quickly engage end-users, and provide an intuitive experience and insights.

Among various visualization tools available in the market, SAP Lumira has an edge being an SAP tool where end-user consumption of analytical applications is governed and secured by the SAP Business Objects BI Platform.

Extending the Dashboard Functionality

Lumira designer provides extensive customizations through scripting, styling with CSS and above all, the integration of external SDK Components makes it a pinnacle tool to achieve the desired functionalities.

Like any other technology, dashboards are constantly evolving, with versatility and impactful ability of integrating SDK components assisting the rapidly developing scope and scale of visualizations for the organizations.

Along those lines, Business users expect the ability to modify the data that lies behind a visualization component by providing data inputs to the dashboard while analyzing the data and anticipate the changes to be reflected immediately in the dashboard.

Lumira designer leverages support for updating or modifying the data in underlying database through write back functionality.

Benefits of write back in the dashboard:

  • It transforms a traditional dashboard to Interactive analytical application which supports business data modifications
  • It allows data analysis and data update from the same dashboard, rather switching over different applications for each task

SDK Extensions

SDK stands for Software Development Kit. SDK is set of tools, libraries, code samples, processes and guides that allows developers to create applications on a specific platform.

SDK Extension components can be integrated flawlessly into the core application to utilize its features for the customized product developments. The visualization of extension components is based on HTML, JavaScript and CSS.

Web Application Programming Interface

Web API is an Application Programming Interface over the web which can be accessed using HTTP protocol.

Web API is an extensible framework for building HTTP based services that can be accessed in different applications on different platforms such as web, windows, mobile etc.

Integrating Web APIs into the Lumira designer enhances the dashboard functionality by adding abilities not offered in the baseline version of the tool, such as providing the possibility of writing back to the source database directly from the dashboard itself.

Post Response Parser

Post Response Parser is an SDK Extension, with which you can model your application to make AJAX (Asynchronous JavaScript and XML) calls to any Web API and evaluate the response for desired interactivity in Lumira designer.

Feature of Post Response Parser:

  • Opens a request via AJAX call to any specific URL
  • Accepts parameters along the Request
  • Supports BIAL (BI Action Language) Scripting for interactive control at runtime

Business Use Case

In Banking, Credit Control & Monitoring department uses exception reports on their day to day operations for the analysis of their customers credit performance. Based on the outcomes, the team decides on the action to be taken for the respective customers with the various levels of audits.

Business Team faces challenges to maintain and track the remarks and comments on each customer by looking at the reports. So CCM wants to develop a dashboard with the ability to update their observations and comments on the same dashboard which in turn gets stored in database.

Lumira designer provides sub-optimal workarounds for capturing the filters and remarks with technical components like Bookmarks and Comments which comes along with the core application, but these components cannot not write back to the database, but incorporating  SDK Extensions along with the core would be able to achieve the desired customization in the dashboard application.

Functionality and Process Flow

The Post Response parser integrates external Web API into the Lumira designer, this SDK extension passes the parameters from the dashboard to the underlying stored procedure in Web API which in turn updates to the database.

Snippet of process to be followed:

  • Install Post Response Parser SDK Extension at client and server system
  • Encapsulate the parameters as global variable and enable its property to expose as URL Parameter
  • Create a Web service for dashboard to accommodate the database updates
  • Define an event to trigger the SDK Extension in Lumira application
  • Reload the data source through script to reflect the changes in dashboard

Conclusion

Lumira designer is competent to build Business Intelligence Applications that can be dynamic and customizable as per the business users’ workflow.

An interactive prototype is the best way for both users and designers to learn about their specific needs.

In conclusion, Lumira Designer with SDK Extensions offers that capabilities to accommodate our design process and it stands strong in its ability to build simple or complex Analytic Applications and Executive Dashboards.

About the Author:

Kashif is a SAP Business objects consultant and a business analytics enthusiast. He believes “Ultimate goal is not about winning, but to reach within the depth of capabilities and to compete against yourself to be better than what you are today.”

Machine Learning from Programmer’s Perspective

Gireesh Sreedhar KP

Introduction

Machine Learning (ML) is key pillar of the Artificial Intelligence (AI) domain. ML solves problems which are unimaginable using traditional programming paradigm. During my interactions with people on ML, I am frequently asked following key fundamental questions.

  1. What is Machine Learning (ML)?
  2. What is the need for ML programs when traditional programs have served us well for decades?
  3. What differentiates ML from traditional programming paradigm?

Let me answer above questions from a programmer’s perspective to build understanding irrespective of your ML background.

Traditional Programming Paradigm

We are familiar with traditional programming, where we use selected programming language (like C, Java, etc.) and program specific instruction or rules to process inputs which creates output we need.

Let us understand with an example, a retail store wants to write a program to find amount to be paid (Amount) given Quantity (q) and price per unit (p). We will solve this by writing code as below.

  1. Read two inputs ‘q’ and ‘p’ (Data)
  2. Amount = p*q (apply Rules, Rules are part of program, but shown as input for illustration)
  3. Return Amount (Output)

The need for Machine Learning

Let us try to solve same problem of computing ‘Amount’ from inputs ‘p’ and ‘q’. However this time we are required to read the inputs (p and q) from a piece of paper with digits either handwritten or printed. This needs program to recognize the digits from paper (images of digits received by program) before digits can be assigned to ‘p’ and ‘q’.

Let us examine traditional programming approach (writing rules) to recognize the images of digits received by program

  • Are rules scalable?
  • Can rules handle recognizing digits written in different orientations and styles? Say, when image received is program should recognize the image as digit 8.
  • There are over 70,000 samples of handwritten digits which are commonly used (refer MNIST database, sample below), can we write rules to cover all possible combinations?

Now it’s clear to us that rules-based approach will break and it’s not practical to build all rules and program those. We need something else instead of rules to solve these types of problems and that something else which replaces rules is Machine Learning.

What is Machine Learning?

Let us ask ourselves

  1. What differentiates the first problem statement (easily solved using rules) from the second one?
  2. Why a problem easily solved by humans (recognizing different styles digits by vision), is such a difficult task for computers?

We humans learn to identify digits which are written in standard format, however when presented with digits written in different styles and orientations, we are still able to recognize the digits identifying the patterns which are the beauty of human learning process. Can we make computers (machines) do the same and learn like humans? Let us understand how we make a machine to learn this task and perform like humans.

We will feed the Machine Learning program (ML) with lots of data (examples) containing images of digits in different styles and orientations along with actual digit it represents (supervised learning). Say one data point will be an image and mapped to corresponding digit 8. We are providing data along with the intended output as input to ML for learning. Processing lots of inputs, ML comes up with Rules or Patterns or Models to map an input to output we need (like humans).

This Rules/Pattern/Model learned by ML will be used to process new incoming data to produce output or sometimes called as Predictions.

What differentiates ML from traditional programming paradigm

The major difference between traditional program and ML is, traditional program applies rules on input data to produce output. However, ML takes output (outcomes we need) as input and produces Rules/Pattern/Models as output which are then used to process new inputs.

Why Machine Learning

Data-driven decisions increasingly make the difference between keeping up with the competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.

Machine Learning at GAVS

GAVS has own in-house Artificial Intelligence research team building advanced Machine Learning algorithm and techniques powering its products and solutions. ZIF (Zero Incident FrameworkTM) Artificial Intelligence-based Technology Operations (AIOps) from GAVS is powered by state-of-the-art Machine Learning algorithms developed in house.

About the Author:

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.