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MLOps – ML Production Revolution

This blog is to stress the importance of MLOps in the ML project lifecycle, assert the necessity of MLOps in the context of the entire Software industry, explain its relevance within the context of DevOps, and how the changing Software development environment and processes help the ascend towards the peak of the Digital Revolution. 

MLOps – What’s that got to do with Production or Revolution?

To understand this relation, we need to go through a bit of history and some examples of how inventions and innovations revolutionized the technology industry, which has, in turn, revolutionized several other industries. We’ll put this in the context of Machine Learning and MLOps, and explain the rationale behind the necessity to put more effort and focus into it.  

There are many jargons, acronyms, noise, and clutter around this space that decision makers are finding it hard to come to a conclusion on when and where to invest.  

The top 3 questions and concerns that we hear from business stakeholders when we pitch the importance of MLOps are: 

  • MLOps seems to be much more intense than the Development Operations we have in house. Why can’t this be integrated to the existing DevOps cycle? 
  • Can we consider MLOps once after we vet the success of ML projects or ideas?  
  • Cost of implementing MLOps right away might outweigh the budget for the ML PoC or idea. How can we justify this additional spending?   

This article aims to alleviate these concerns and make these questions self-explanatory by stressing the importance and necessity of MLOps.  

To understand the significance of the term “production” in the title, let’s delve a bit into the history of the Industrial Revolution. This will provide the context for the argument. 

The first industrial revolution started with water and steam, and created a disruption in how mechanical production happened. Steam powered machines and machine tools revolutionized how tasks were accomplished, and how people got from one place to another. This was the beginning of the factory, and the transition to new manufacturing processes.

The technological revolution saw rapid progress in science, applied science and technology in mass production. Factories, production lines and processes vastly improved with the advent of gasoline engines. 

Fig1. VW Beetle Assembly Line

The digital revolution – saw groundbreaking inventions happening with semiconductors, Integrated Circuits (ICs), miniaturization, microprocessors, affordable computing device form factors, wireless technologies, internet etc. This was the beginning of the Information age and most importantly this was the time when Software came to be recognized as a product, and this outlook changed the entire dynamics of this age.  

Fig 2. Software Product

The exponential growth in the digital age happened due to Software. Once we arrived at a point where we were able to create the right kind of generic computing hardware for the software to shine on, things started moving at an exponential pace. 

Every industry has to write code and program their systems in some form or the other. Every device / equipment that you see in any domain is heavily software driven. Take for e.g. EDA, CAD/CAM, Physical design tools, hardware description languages like VHDL or Verilog, AV workflows, broadcasts, content delivery, just to name a few. Hardware and Chip design, development & manufacturing workflows are heavily software driven, and this led to much improved platforms to run even better Software, which led to an optimizing cycle.  

Emergence of a common theme

If closely analyzed, we can see a pattern emerging across all the industrial revolution phases. Each one starts with mechanization and automation using new discoveries or inventions. But, across each phase we see a period of drastic improvement to the factory and production line prevalent during that phase. These factory and production line advancements lead to the pinnacle of each phase, until they are completely surpassed by a totally new invention.  

For example, it was the James Watt steam engine that upraised the first phase, the internal combustion engine revolutionized the second, and now Software is changing the third. Since the current digital phase is primarily Software driven, this summit can only happen through advancements in Software development and delivery process. That is, the very Software factory that produces Software. This has to span across industries and domains. 

The way we do Software Development now is vastly different from what was the norm 10 years ago. Alongside that we are also witnessing the emergence of new Software development paradigms like Deep Learning helping us reach new areas and fill gaps, which we thought would never be possible with Software. Let’s now talk about this new Software paradigm and the improved Software factory and production lines. 

Fig 3. Illustrating DevOps

Enter the DevOps Era
DevOps is now considered a standard practice with or without its knowledge! Development teams across the globe have started the practice knowingly or unknowingly! It is slowly turning out to be the de facto way of Software Development and Delivery. There is still a long way to go before DevOps gets fully adopted, and the industry as a whole is starting to benefit from the rapid pace and quality of Software. 

DevOps is not a single person’s job or a single team’s job. It’s not a Job title, because it is not a job function, role, or technology per se. It is a collaborative methodology for doing Software Development and Delivery. Over the past few years the amount of quality tooling, processes, and workflows that got introduced to the development and production line, have made significant improvements to the way Software is produced and delivered. As noted earlier, mostly everyone in every industry is writing Software one way or the other, and this trend is going to go up exponentially.  

Machine Learning, a new Software paradigm  

In the Artificial Intelligence parlance, there are different approaches like Symbolic, Deep Learning, and hybrid ones like the IBM Watson. When we use the term AI, ML or Deep Learning in this article, we mean “Supervised Learning”, which is one form of Deep Learning. Other AI and ML approaches might need radically different computing platforms, development, delivery and operational workflows which is beyond the scope of this article.  

Supervised learning is basically learning an X -> Y mapping, a function that maps an input to an output based on example input-output pairs.  

With the supervised learning approach there is a paradigm shift happening with how we program computers, and basically this is changing our relationship to computers from a development and usability perspective. Instead of programming computers, the ML approach is to show them, and let them figure it out. That’s a completely different way of developing Software, when whole industries are built around the idea of programming computers. Educational institutions and Corporations are only now slowly catching up to this paradigm shift.  

Fig 4. Classic Software vs. ML

So what needs to be shown, and what is there to figure out

In simple terms, we need to show the data and the mapping (X -> Y), and what is being figured out is an approximate function for the mapping. X denotes the feature vectors from the input data and Y can be thought of as the labels for the ground truths. The approximation functions are figured out using back propagation algorithms like Gradient Descent, Momentum, Adam, RMSprop etc. These algorithms continuously try to optimize the weights and bias factors by minimizing the cost or loss function for the entire training set. The goal is to identify weight factors that make the convex cost function go down the slope, and reach the global optima as quickly as possible. This should be optimized for the entire training sample. This is a mouthful, but in very simple terms you need to show the data and mapping or labels to the computer, and the algorithms will learn this mapping as an approximation function, which we can later use for prediction.  

Fig 5. Minimizing the cost function

But, how does this actually work?

Nobody clearly knows, and people have drawn analogies to how the brain learns etc. which is a bit far-fetched imagination.  

But the fact is, this “forward-prop” & “back-prop” method used in Supervised Learning has turned out to be a very good way for finding an approximation function, and this function will work quite well, subject to certain conditions. These conditions form the pillars for our discussion forward. Recently researchers and big internet companies have shown us that supervised learning has worked brilliantly well especially with some unstructured use cases, like image, audio, video, speech, NLP etc.  where traditionally computer algorithms (expert systems) were not that good.  

Deep supervised learning is a very empirical and iterative process and by “empirical process” we mean – you just have to try a lot of things and see what works. There is no magic bullet! Remember we’re trying to learn an approximation function. Since this is entirely data driven, it will always be evolving. Data is the fuel here. For compliance reasons, even the explanation for a prediction or result from a neural network model needs to be done in data parlance.  

Fig 6. From Model-centric to Data-centric AI by Andrew Ng

TL;DR: What you need to show the computer is a lot of good data and mapping (labels), and what they are figuring out is an approximation function.

So, isn’t this Software? 

Yes, it is Software, and it also needs tons of typical software and hardware around it to work. It’s a different way of programming, the key factors being…  

  • Data is the fuel here 
  • Labelling is the labor here 
  • Experimentation is the process here   

And maybe we can also add this… 

  • Weaving networks differently is the research here 

All of this is very much iterative & empirical in nature, much more than typical SDLC (Software Development Lifecycle). The empirical nature of the Model Development Life Cycle is much more of a necessity than typical SDLC. This cycle needs to happen at a much more agile pace, needs to be monitored & measured continuously, can never stop because of data evolution or seasonality, needs to be sensitive, needs to be responsible and the list goes on. It’s not like typical Software processes don’t need any of these; In ML this is a necessity.  So hope you are getting a sense of where we’re going with this, and why we’re pitching the importance of MLOps to keep all of this running and improving. Please keep reading as the pitch will be more clear and evident after some more points.  

Analogy to make the ML paradigm apparent 

When we talk or write in English, do we always think and apply the rules of the English grammar? I am a non-native English speaker and I never learned English grammar by the rule-based approach (symbolic approach)! Then, how do we speak or write without explicitly studying or knowing any of these? It’s because the brain might have heard, read, and got trained, and developed some sort of an approximation strategy. More experience means more data, and the better we get without explicitly knowing all the rules.Page Break 

This is a very crude analogy of supervised learning and one shouldn’t draw analogies to the human brain functions from this example. Consider ML as naive neuroscience, just like genetic algorithms are naive biology! 🙂     

So what’s MLOps then? 

If you’ve come this far, you might have understood that we need tons of good data, continuous monitoring, continuous training, and continuous experimentation, or in short continuous operations to make ML work. It’s empirical, iterative & data centric by nature. There is no fixed forward function which you know upfront how to program! You don’t know that function, so you need to derive approximation functions using forward-, back-prop techniques, and for that you need to train/fit the neural network model with data & labels. But the world (data, labels, truths) evolves, and therefore for the function to stay relevant the entire cycle of operations (data collection, mapping, labelling, feature engineering, training, tuning etc.) needs to churn along. This is a necessity in the case of ML projects! Learning and adapting continuously is the simple mantra behind successful Deep Learning or ML projects.  

This operational cycle is called “MLOps”. Without this optimizing cycle there is no relevance for ML projects or ideas. 

Fig 7. MLOps Principles

MLOps is not a single person’s job or a single team’s job. It shouldn’t be used as Job title, because it is not a job function, role, or technology per se. It is a collaborative, iterative, empirical, and data centric methodology for doing Machine Learning Model Development and Delivery. 

Stressing the Significance of ML projects and MLOps

To understand the significance of ML projects and therefore MLOps, we’ll take a slight detour. Earlier we talked about two fundamental approaches, programming functions and learning functions. Some questions arising here are…  

Will the two fundamental programming paradigms coexist? Or will it replace the Software expert systems, symbolic representations, and the traditional Software development in general?  

Proponents of ML have argued the need for more people who get computers to do things by showing them. Large corporations like Google are now training people, called brain residence. A lot of new aspiring engineers actually want to work on ML.   

But, both these approaches are going to coexist in the future. Even in the AI space the symbolic and non-symbolic representations will co-exist. This coexistence is necessary because… 

  • There are certain areas where the classic approach shines. 
  • There are other areas where classic approaches lack, especially the unstructured ones, like vision, NLP, translations, object detection, image classification etc. where the Human Level Performance outshines. ML will fill this gap. 
  • There are cases where we still don’t know, or have not developed the function to program. So we need to learn those functions from existing data and labels. 
  • There might be yet other use cases where ML approaches may be used to start with, and then these networks and their connections and weights will be analyzed and studied to deduce a generic function. 

In fact if you see it from another angle, the feature engineering and feature extraction work that is given much importance in supervised learning can be thought of as a step towards designing an expert or classic system. Data scientist / domain expert studies, analyzes the data to extract the features that they think might have significant impact on the results of the model. It should be clear by now, why MLOps is called a “data centric” methodology. What makes MLOps a necessity is the empirical, iterative, and data centric nature of supervised machine learning to sustain its relevance. This makes MLOps much more intense than the typical DevOps cycle we are used to.  

Both Software and ML development & release workflows are super essential for revolutionizing the grand Software production line. Efficient DevOps and MLOps practices and tooling will be key to climb the summit of the Digital Revolution. This is true for all industries and all domains.   

What if there is no proper MLOps? 

By this time, you might have already understood the issues with not having a production line workflow and tooling for ML projects and software projects in general. There are many facets to it, but we’ll briefly touch upon some of the most important ones. 

Lack of proper experimentation tracking – will lead to chaos for data scientists and ML engineers, and is a recipe for underperforming Models in production. As we’ve repeated throughout the article, supervised ML is a highly iterative, data intensive, highly empirical process. If there aren’t enough tooling and processes to track and analyze these experiments during development and operations, it’s simply not going to work.  

Model irrelevance – will be the direct result of not having an end to end streamlined MLOps workflow. For ML projects, the most important mantra to remember is that the real development starts with the first deployment. When the model starts to see the real world data, it’s going to be a different story altogether. If you didn’t have a process or workflow to monitor the model performance w.r.t input and output metrics, measure the drifts, detect outliers, or if you didn’t have a way to collect and ingest real data back into the workflow, and retrain the model, the model is not going to stay relevant for long. This will have a direct impact on the business if it’s relying on this model for its core operations. So MLOps shouldn’t be an afterthought, it should be set up right alongside the first lines of code or data that you develop or collect. 

Cost effectiveness – should be top priority for any ML project. Afterall primary aim for any ML project is to meet or even exceed Human Level Performance (HLP). This is true for use cases which humans were typically good at, and also for those tasks where humans typically relied on computers. The main fuel here is data, mapping, features that can be engineered out of the data. The cost impact of these resources and processes is a very important question. For that 0.1% improvement, if it’s going to drain the pocket by an additional 10%, does it make any sense? This could be development or operational costs like infrastructure scale cost, data storage costs, data transformation costs, training compute costs, inference costs, specialized hardware cycles etc. Without MLOps processes, tooling, and workflow there’s no way to measure and quantify these metrics and act upon them in an iterative manner. Without MLOps there’s no way to identify whether an ML idea is worth pursuing, and quantifying its cost effectiveness. 

Implementing MLOps is definitely going to cost an organization for the tooling, infrastructure etc. But the long term benefits and savings it brings about far outweighs its cost. Over time, organizations should develop, unify, and enforce standard MLOps tools & practices across many ML projects and teams. This is key to avoiding disasters down the line. 


Without continuous Development and Operations, there is no relevance for ML projects. It’s so much more critical for ML projects due to its nature. MLOps tooling, workflows, processes, pipelines, etc. should be set up right alongside experimentation of the project idea. Common MLOps guidelines, infrastructure, tooling environment etc. across multiple projects in an organization is also critical for streamlining and cost reduction. Think of supervised deep learning as one big Continuous Experimentation, a Lab that runs forever. MLOps is only the way to tame this highly iterative, empirical, data intensive genre of Software development. Every industrial revolution started with new discoveries and paradigm shifts in production, and then reached their summit by advancements in these production lines. We see this grand DevOps space with MLOps being an integral part of it, as the cornerstone for production line optimization which will take the current Digital phase to its pinnacle.  

In the next set of blogs, we’ll delve deeper into what needs to be done differently, pipelines, lineage, provenance, experiment tracking, benchmarking, continuous integration / continuous deployment / continuous monitoring / continuous training, responsible data handling, how tools can help streamline the process, how much automation is good, and much more. So stay tuned… 

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Human Pose Detection & Classification

Some Buildings in a city


  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

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OCR / Pattern Recognition

Some Buildings in a city

Use cases :

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Highlights :

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Behavior Monitoring

Some Buildings in a city

Use cases :

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Highlights :

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Attire & PPE Detection

Some Buildings in a city

Use cases :

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Use cases :

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  • Post-deployment trainable


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    Real Time Color Detection​

    Use cases :

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    Highlights :

    • Color detection algorithm with real time performance
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    Missing Artifact Detection

    Use cases :

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    Highlights :

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    Real Time Manufacturing Line Inspection

    Use cases :

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    Highlights :

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    Ground Based Infrastructure analytics

    Some Buildings in a city

    Use cases :

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    Co-founder & CEO


    Founder and Managing director of Ignitarium, Sanjay has been responsible for defining Ignitarium’s core values, which encompass the organisation’s approach towards clients, partners, and all internal stakeholders, and in establishing an innovation and value-driven organisational culture.


    Prior to founding Ignitarium in 2012, Sanjay spent the initial 22 years of his career with the VLSI and Systems Business unit at Wipro Technologies. In his formative years, Sanjay worked in diverse engineering roles in Electronic hardware design, ASIC design, and custom library development. Sanjay later handled a flagship – multi-million dollar, 600-engineer strong – Semiconductor & Embedded account owning complete Delivery and Business responsibility.


    Sanjay graduated in Electronics and Communication Engineering from College of Engineering, Trivandrum, and has a Postgraduate degree in Microelectronics from BITS Pilani.


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      RAMESH EMANI Board Member


      Board Member

      Ramesh was the Founder and CEO of Insta Health Solutions, a software products company focused on providing complete hospital and clinic management solutions for hospitals and clinics in India, the Middle East, Southeast Asia, and Africa. He raised Series A funds from Inventus Capital and then subsequently sold the company to Practo Technologies, India. Post-sale, he held the role of SVP and Head of the Insta BU for 4 years. He has now retired from full-time employment and is working as a consultant and board member.


      Prior to Insta, Ramesh had a 25-year-long career at Wipro Technologies where he was the President of the $1B Telecom and Product Engineering Solutions business heading a team of 19,000 people with a truly global operations footprint. Among his other key roles at Wipro, he was a member of Wipro's Corporate Executive Council and was Chief Technology Officer.


      Ramesh is also an Independent Board Member of eMIDs Technologies, a $100M IT services company focused on the healthcare vertical with market presence in the US and India.


      Ramesh holds an M-Tech in Computer Science from IIT-Kanpur.


      General Manager - Marketing

      A professional with a 14-year track record in technology marketing, Malavika heads marketing in Ignitarium. Responsible for all branding, positioning and promotional initiatives in the company, she has collaborated with technical and business teams to further strengthen Ignitarium's positioning as a key E R&D services player in the ecosystem.

      Prior to Ignitarium, Malavika has worked in with multiple global tech startups and IT consulting companies as a marketing consultant. Earlier, she headed marketing for the Semiconductor & Systems BU at Wipro Technologies and worked at IBM in their application software division.

      Malavika completed her MBA in Marketing from SCMHRD, Pune, and holds a B.E. degree in Telecommunications from RVCE, Bengaluru.



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      Pradeep comes with an overall experience of 26 years across IT services and Academia. In his previous role at Virtusa, he played the role of Delivery Leader for the Middle East geography. He has handled complex delivery projects including the transition of large engagements, account management, and setting up new delivery centers.

      Pradeep graduated in Industrial Engineering and Management, went on to secure an MBA from CUSAT, and cleared UGN Net in Management. He also had teaching stints at his alma mater, CUSAT, and other management institutes like DCSMAT. A certified P3O (Portfolio, Program & Project Management) from the Office of Government Commerce, UK, Pradeep has been recognized for key contributions in the Management domain, at his previous organizations, Wipro & Virtusa.

      In his role as the Head of Operations at Ignitarium, Pradeep leads and manages operational functions such as Resource Management, Procurement, Facilities, IT Infrastructure, and Program Management office.


      SONA MATHEW Director – Human Resources


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      Sona heads Human Resource functions - Employee Engagement, HR Operations and Learning & Development – at Ignitarium. Her expertise include deep and broad experience in strategic people initiatives, performance management, talent transformation, talent acquisition, people engagement & compliance in the Information Technology & Services industry.


      Prior to Ignitarium, Sona has had held diverse HR responsibilities at Litmus7, Cognizant and Wipro.


      Sona graduated in Commerce from St. Xaviers College and did her MBA in HR from PSG College of Technology.



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      As VP of Sales, Ashwin is responsible for Ignitarium’s go-to-market strategy, business, client relationships, and customer success in the Americas. He brings in over a couple of decades of experience, mainly in the product engineering space with customers from a wide spectrum of industries, especially in the Hi-Tech/semiconductor and telecom verticals.


      Ashwin has worked with the likes of Wipro, GlobalLogic, and Mastek, wherein unconventional and creative business models were used to bring in non-linear revenue. He has strategically diversified, de-risked, and grown his portfolios during his sales career.


      Ashwin strongly believes in the customer-first approach and works to add value and enhance the experiences of our customers.


      AZIF SALY Director – Sales


      Vice President – Sales & Business Development

      Azif is responsible for go-to-market strategy, business development and sales at Ignitarium. Azif has over 14 years of cross-functional experience in the semiconductor product & service spaces and has held senior positions in global client management, strategic account management and business development. An IIM-K alumnus, he has been associated with Wipro, Nokia and Sankalp in the past.


      Azif handled key accounts and sales process initiatives at Sankalp Semiconductors. Azif has pursued entrepreneurial interests in the past and was associated with multiple start-ups in various executive roles. His start-up was successful in raising seed funds from Nokia, India. During his tenure at Nokia, he played a key role in driving product evangelism and customer success functions for the multimedia division.


      At Wipro, he was involved in customer engagement with global customers in APAC and US.


      RAJU KUNNATH Vice President – Enterprise & Mobility


      Distinguished Engineer – Digital

      At Ignitarium, Raju's charter is to architect world class Digital solutions at the confluence of Edge, Cloud and Analytics. Raju has over 25 years of experience in the field of Telecom, Mobility and Cloud. Prior to Ignitarium, he worked at Nokia India Pvt. Ltd. and Sasken Communication Technologies in various leadership positions and was responsible for the delivery of various developer platforms and products.


      Raju graduated in Electronics Engineering from Model Engineering College, Cochin and has an Executive Post Graduate Program (EPGP) in Strategy and Finance from IIM Kozhikode.


      PRADEEP SUKUMARAN Vice President – Business Strategy & Marketing


      Vice President - Software Engineering

      Pradeep heads the Software Engineering division, with a charter to build and grow a world-beating delivery team. He is responsible for all the software functions, which includes embedded & automotive software, multimedia, and AI & Digital services

      At Ignitarium, he was previously part of the sales and marketing team with a special focus on generating a sales pipeline for Vision Intelligence products and services, working with worldwide field sales & partner ecosystems in the U.S  Europe, and APAC.

      Prior to joining Ignitarium in 2017, Pradeep was Senior Solutions Architect at Open-Silicon, an ASIC design house. At Open-Silicon, where he spent a good five years, Pradeep was responsible for Front-end, FPGA, and embedded SW business development, marketing & technical sales and also drove the IoT R&D roadmap. Pradeep started his professional career in 2000 at Sasken, where he worked for 11 years, primarily as an embedded multimedia expert, and then went on to lead the Multimedia software IP team.

      Pradeep is a graduate in Electronics & Communication from RVCE, Bangalore.


      SUJEET SREENIVASAN Vice President – Embedded


      Vice President – Automotive Technology


      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.


      RAJIN RAVIMONY Distinguished Engineer


      Distinguished Engineer


      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.


      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.


      Rajin holds an MS in Micro-electronics from BITS Pilani.


      SIBY ABRAHAM Executive Vice President, Strategy


      Executive Vice President, Strategy


      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.


      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.


      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.


      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut


      SUJEETH JOSEPH Chief Product Officer


      Chief Technology Officer


      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.


      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.


      He graduated in Electronics Engineering from Bombay University in 1991.


      SUJITH MATHEW IYPE Co-founder & CTO


      Co-founder & COO


      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.


      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".


      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.


      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.


      RAMESH SHANMUGHAM Co-founder & COO


      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.