In this episode of Leading with Data, we’re joined by Dr. Kiran R, a distinguished leader in Applied ML and Data Science, who shares insights from his extensive experience at Microsoft, VMware, and beyond. With a Ph.D. from IIM Lucknow and an MBA from IIM Kozhikode, Dr. Kiran has led high-impact teams, earning accolades such as “Innovator of the Year.” Join us as we explore his expertise and innovative approaches in ML.

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Key Insights of our Conversation with Dr. Kiran R

• Generative AI has made significant strides in simplifying NLP-related problems, shifting the focus from ML science to ML engineering.
• The corporate drive towards Generative AI is fueled by a desire to be seen as futuristic and innovative, affecting project priorities and investments.
• Kiran’s diverse experience across companies like Dell, Amazon, and VMware has culminated in his current role at Microsoft, where he’s shaping the future of ML engineering.
• Microsoft’s internal center of excellence focuses on applied ML, anomaly detection, and leveraging Generative AI for partner and sales enhancements.
• The future of Generative AI lies in its ability to automate end-to-end processes across various industries, creating new opportunities for innovation.
• Aspiring data scientists should focus on continuous learning, technical and business understanding, and practical application of their skills.

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Let’s look into the details of our conversation with Dr. Kiran R!

How is Generative AI Revolutionizing Problem-Solving in Data Science?

Generative AI has indeed transformed the landscape of problem-solving, particularly in the realm of Natural Language Processing (NLP). It has simplified a class of problems, making them much more approachable. For instance, chatbot creation has shifted from a complex ML science problem to an ML engineering task, thanks to Generative AI. It’s not just about the ease; it’s about the shift in our approach to these problems, opening up a new dimension of possibilities.

What’s Driving the Demand for Generative AI in the Industry?

The excitement around Generative AI is palpable across the board. There’s a sense that if you’re not investing in this technology, you’re missing out on the future. This perception has real consequences in the corporate world, influencing promotions and job security. As a result, there’s a surge in demand, with half of our project mix now pivoting towards Generative AI applications.

Can You Share Your Journey from the Early Days of Data Science to Your Current Role at Microsoft?

My journey began before the term ‘data scientist’ was even popular. I’ve always been drawn to the intersection of programming, computer science, statistics, and business. Starting my career at Motorola and then pursuing an MBA from IIM Lucknow, I found my niche in analytics at Dell. Over time, I’ve held various roles, from an advisor to a senior director, across companies like Amazon and VMware. Now, at Microsoft, I’m focused on ML engineering for cloud data sciences, where I’m building MLOps platforms and contributing to enterprise ML.

What Does Your Role at Microsoft Entail, and How Does It Impact Clients?

At Microsoft, I’m part of the Cloud Data Sciences group, which is akin to an internal center of excellence. We tackle a variety of problems, from applied ML in engineering systems to anomaly detection and commerce. We also work on enterprise ML, creating models for propensity and recommendation systems that aid sellers and partners. Additionally, we leverage Generative AI to enhance partner profiles on the Azure marketplace and simplify sales processes.

How Has Generative AI Impacted Your Work at Microsoft?

Generative AI has significantly shifted our project focus, with a substantial portion now dedicated to this domain. It’s particularly effective in NLP-related tasks, streamlining processes like chatbot development and enterprise search. However, it’s crucial to understand its limitations and not get swept up in the hype. Generative AI is a powerful tool, but it’s not a magic wand that will replace all programming or decision-making.

Looking Ahead, What Breakthroughs in Generative AI Are You Most Excited About?

In the next few years, I anticipate a move towards more integrated and end-to-end automation using Generative AI. This could revolutionize industries like healthcare and finance by automating routine tasks and enhancing decision-making processes. The potential for startups and established companies alike is enormous, as they can leverage this technology to create innovative solutions and services.

What Advice Would You Give to Technical Experts Looking to Make an Impact in AI and ML?

For those looking to excel in AI and ML, it’s essential to understand both the technical and business aspects. Stay curious, keep learning, and don’t shy away from asking questions. Engage with stakeholders, understand the data flow, and stay abreast of the latest tools and techniques. It’s also vital to have strong debugging skills and a never-give-up attitude.

Can You Recommend Any Resources for Aspiring Data Scientists?

I recommend practical resources like Kaggle for modeling insights, books like “Practical Deep Learning for Coders” by Jeremy Howard for deep learning, and “Python for Data Analysis” by Wes McKinney for mastering Python and pandas. Additionally, staying informed through platforms like Analytics Vidhya and engaging with the latest research papers on Google Scholar can be incredibly beneficial.

Summing-up

Our conversation with Dr. Kiran R has shed light on the transformative power of Generative AI in data science and its implications for the industry. Dr. Kiran’s vast experience, spanning from Motorola to Microsoft, provides invaluable insights into the evolving landscape of ML engineering. As Generative AI continues to shape the future of problem-solving, Dr. Kiran emphasizes the importance of continuous learning and practical application for aspiring data scientists. With his guidance and recommended resources, the journey towards mastering AI and ML becomes more accessible.

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