Product Owners in Omdena’s AI Challenges – Developing My Career

Data Science pipelines are a collaborative work of a big team of data scientists, machine learning engineers, and data analysts. Such teams require good leadership and project management skills. Product owners/Project managers in Omdena add a wide range of management skills to their careers by organizing and directing the completion of the projects while ensuring these projects are on time, with quality, and within scope.

Let’s introduce Saisreenath Srinivasan Govindarajan, one of the successful product owners in Omdena.

Can you briefly introduce yourself, Sai?

I am a Technology Leader/Manager and Solutions Architect with around 13 years of industry expertise in Software engineering, Cloud technologies, and Artificial Intelligence, happen to work with some of the large Consulting and Product firms across geographies and cultures. Seasoned in executing many successful engagements end-to-end, creating an Innovation culture, and driving agile teams towards product delivery. I love adventures and keep trained to be prepared for facing unexpected challenges. I am a  kiddish father, selective hearing husband, and a bad guitarist:-). 

How did you end up in AI?

Since college, I have had a thirst for AI. I was Microsoft Student Partner representing our college and had opportunities to bridge student communities with Microsoft, working on some of the Speech and Image Processing technologies then and truly believed AI is the currently accessible technology we have to make our dreams and Innovations a reality to make the world a better place for everyone….. Later I had been working on many side projects related to AI  individually and with a team of like-minded friends, taking many courses and certifications from MIT, IBM, etc. but did not have a chance to work on an industrial scale. 2 years back I joined a leading Market Intelligence company where I started again to work with AI Engineering, Machine learning, NLP  and building Big Data pipelines, which revived and accelerated my learnings and journey towards AI. 

Which AI projects have you participated in?

First, tell us how you knew about Omdena

Well, the first time I heard of Omdena was from my wife, as part of her MBA works on market research, she was gathering information on AI organizations making a social impact, inevitably she came across Omdena, and since she knew I am passionate about creating social impact with AI, advised me to take a look, and from then no turning back :-)… 

I am currently finishing up as a Project Manager with Improving Transparency and Democratizing Access to Public Sector Contracts using NLP Project –  where we are democratizing public contracts with NLP. The desire is to build better communities by improving a broken procurement process where incumbents win the majority of the contracts.

And will be starting up managing another upcoming Project – Preventing Food Waste by building a Forecasting Model to Predict Sales of Fresh Food

How did the Omdena experience as a Product Owner help you in your career?

In a world of WHAT, WHY, and HOW, I strongly believe the intensity you have towards WHAT and WHY stands to be the driving factor that defines the depth of your Innovation on HOW. Being into technology management (HOW) for over a decade and working with some of the large consulting and product firms had helped me a lot in setting a good base in technology and management, but a dream of an individual who always wanted to create a social impact with technologies had been in the back seat. 

Omdena happened to be that magical bridge for me connecting my passion for building disruptive AI-driven products for social good and transitioning my career towards WHAT and WHY as a Product Manager.  As a Product Owner/Manager with Omdena, you lead a change initiative by directly working with the AI startups, organizations, and leaders who are passionate about creating products that can transform the communities and world, infer their strategic vision, and work with them in building AI roadmaps, strategizing and creating MVPs. 

The best part of Omdena is the collaborative bottom-up approach where you serve to be a leader working with 50+ change-makers (Data Scientists, Machine Learning Engineers, and Researchers) from various industry segments, countries, cultures, and time zones but with a common goal for social good. This helps the Startups/Organizations to get a head start into Artificial Intelligence, solve real-world problems, and build deployable solutions within a short time frame.  It challenges and improves your decision-making skills, design thinking & planning, prioritizations, customer focus, domain expertise, and influencing teams without authority. 

Omdena’s experience has greatly helped me in transitioning from a Technology leader towards an AI Product leader. It gave me a deeper understanding of building efficient Machine learning, Deep learning models, ML pipelines, productizing with MLOps, and going over an entire AI-Product life cycle which is pretty much different from a conventional Product life cycle. It also helped me innovate, build and adopt an effective product execution framework and strategies to create a better environment and culture to work with large teams towards product delivery. Leading such large teams directly, of 50+ highly talented Data scientists and ML Engineers to generate synergy and converge towards the social impact has made me more humbled and responsible as a better person and leader. It was starting to live out my passion towards the social path, creating a better world.

A heartfelt thanks to Rudradeb and Omdena for creating such an incredible platform.

Join Sai and other tech leaders by managing an AI project here.

What are your goals and next steps?

I am passionate about creating/contributing towards an Inclusive Economy, 2 of the Product lines I chose to contribute are, 

1) AI for Agriculture (Agri-Tech) – Coming from a family of agricultural backgrounds, I see there is a huge potential or a need or maybe a kind of responsibility for converting Agriculture into a profitable business model with data and AI.  

2) Digital Accessibility – With fast-paced growth in the digital economy, one of the segments I feel still needs to catch up is communities with impairments and I strongly feel AI can bridge this gap and create more job opportunities easily. 

I would try to be around this space either with the support of the organizations I work with or by creating a StartUp to address the opportunity. I am happy that I am already starting to be working towards this journey both from a technology and business domain standpoint. 

Next steps are continuing to scale up in AI and genuinely becoming an empathetic & Inclusive Product leader, re-evaluate how I deal with successes and failures, practicing characteristics on failing fast, measuring and owning the risks, moving from a specialist zone to a generalist zone, and working towards building great teams, seed Innovation, and cultures.

How do you see the future of AI in the IT/Tech industry?

We are in the data-driven age where AI has become an inevitable part of our life, starting with recommending our favorite food, suggesting our favorite movies to watch, to the places we want to travel. In my opinion, we are not far from AI governing an organization by learning the market space, its SWOT, Human resources, and strategizing how to run business and company. The possibilities are endless. Having said that, AI can provide the essential intelligence for the optimal path but not the wisdom, and there would still be human-in-the-loop to make the ultimate decisions. With that, there would be many scopes and an increase in opportunities in the stream of building Responsible and Explainable AI.

AI will play a major role in digital transformation and automation, and this will re-purpose most of the existing jobs to fit into the new AI ecosystem.

It would become imperative for every organization and team to work with AI directly or indirectly. If you compare with cloud computing, probably five years back, there were very few companies and infrastructure teams working on Cloud, span to today there are very few Tech companies and teams not associated with Cloud. AI is not even going to take that long.

At the same time, adoption of AI within organizations is not going to be easy or straightforward as it is just not about the process, technology stack, or skill set changes; instead, it is going to be an entire mindset and cultural change starting with Executive leadership to the Individual teams. So organizations that can strategize and build out a framework to adopt this cultural change will have the edge and a smoother transition.

What would be your advice for people starting new in the field of AI?

If you are a fresher starting new in AI, you are just in time, take a deeper dive and make your basics strong into Mathematics, Statistics, Algorithms, and concepts of Machine learning, Deep learning, dissect the research papers, play with data and build a strong foundation in the field of Data Science. Adoption of technology or tools is the easier part which will anyway to a certain extent be determined by your respective organization strategy in terms of Cloud (AWS/Azure/GCP), Data Engineering pipelines, MLOps, Frameworks, or programming languages, etc. So with your strong basics in Data Science, you can easily navigate through the technology side. Keep your creative nerve spinning, be curious, bring in enthusiasm and fresh energy into the teams, those will be some of the valuable traits. 

If you are a senior professional and trying to shift your career towards AI, be prepared to unlearn and learn, have your teacup empty, which doesn’t mean you have to let go of your existing experience or skill sets, somewhat recalling the philosophy of IKIGAI, you know what you are good at, and you know where you want to go (AI).

Now it’s all about the journey of finding your sweet spot to make the transition. Travel the path with excitement and at your own pace. Following a conventional path or path which might have worked for someone else might be a longer route, so finding your own unique proposition of how you can leverage your existing experience and skills into AI will be the secret sauce. Associate your daily work to the AI field and start from there, for example, if you are into Infrastructure or DevOps, you can start with MLOps; if you are into BI or Analytics, start with EDA techniques; if you are into RDS or Data side take a deeper dive on feature engineering, If you are around API development, you can start with Model serving, Model registry, etc…  so try to find your transition point and then work backward. And certainly with the right attitude, letting go of your senior/expert ego and humility to listen and learn will be the game-changer.

If you are a leader, start working towards creating the next-gen AI leaders, build a safe decentralized environment for them to fail fast and rebuild, create that infinite game, empathizing with the teams, building a cultural intelligence to deal with the diverse workforce, and practicing innovation as part of organization’s DNA is going to be the key. Of course, executing an AI engagement is not going to be like a traditional system like set it and forget it. They are going to need strong governance and maintenance to handle the data drifts and changes in conditions. So a thorough ROI analysis had to be performed, AI implementation roadmap can be different for each company based on their AI maturity levels. Typically it starts with leveraging AI as a service from cloud providers, then working with vendors or consulting firms who work with your resources in building the MVPs and parallel transitioning and building strong in-house AI and Data Science teams.

Collaborative leadership will be the common denominator for all the above segments. The days of competitiveness are history now; we are today in the era of collaboration, where information is shared organically. Everyone takes responsibility for the whole and seeks out diverse opinions and ideas among teams to build strategies and solve problems. This is in contrast to the traditional top-down organizational model, where a small group of executives controls the flow of information. This creates an inclusive environment that energizes teams and cultivates a work culture that is both productive and makes the AI journey seamless for organizations.

Read more success paths towards AI leadership