In my role as a Delivery Director at Prodapt, I’ve been navigating the intricate landscapes of the Telecom, Retail, and Airline domains, leveraging my rich experience in each. This role, one I currently hold, has not only solidified my expertise but has also been the platform for a transformative journey that led me to Great Learning.
The spark ignited when one of my colleagues introduced me to this avenue of learning. My initial foray into the world of Great Learning began with a basic understanding of Machine Learning (ML), and it swiftly blossomed into a bird’s-eye view of the vast expanse within the ML space. This expansion of my knowledge base was unparalleled, providing me with insights that had the potential to reshape my approach to my current role.
However, the most notable shift came with the capstone project. While my current role was familiar terrain, the capstone project required a research-oriented mindset, a realm starkly different from my usual responsibilities. The idea for the project was an extension of concepts shared by Great Learning, but with a twist—we aimed to penetrate a niche market segment that had been relatively unexplored. The challenges that arose were twofold: access to sample data and a reference implementation. Research papers provided glimpses into the kind of data in consideration, while past experiments guided us toward the appropriate models. The suggestion I’d give to fellow learners is to pick a problem that sets them as trailblazers in a particular space. Aligning your capstone project with your domain expertise aids in connecting dots and understanding industry needs. The value of mentors cannot be understated. While interactions were helpful, I’ve found that more face-time with mentors could enhance the experience.
Through this journey, I’ve gained the ability to correlate and engage in meaningful conversations regarding projects implementing AI/ML. My capstone project provided a broader perspective on the job market, especially concerning older adults. While I’m yet to decide on future research work, I’m confident in my capacity to lead ML-based programs, staying ahead of technological advancements and their real-world applications. I’m adept at managing ML-based projects, having honed my skills working with teams on projects like NLP-based chatbot implementations and AI-based operation monitoring platforms. My arsenal includes CI/CD for delivery, TensorFlow, NumPy, and more, tools that helped enhance solutions and accuracy over time. This translates to improved customer experience and satisfaction.
Great Learning’s Industry Expert sessions were invaluable, and the program support was prompt, but I would be remiss not to mention the challenges in the doubt clearance process. Nevertheless, this journey has transformed my perspective, equipping me not only with the technical prowess but also the innovative mindset required to thrive in the ever-evolving world of AI and ML. It’s a journey I’m still on, one that holds the potential to lead to impactful research work and the ability to steer ML-based programs toward success.