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Dr. Kiran R

Partner GM, ML Science & Engineering Leader

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Dr Kiran R is a seasoned ML Sciences & Engineering Executive. As a Partner & General Manager at Microsoft. He leads teams of ML Engineers, ML Scientists, and ML Researchers as the head of Applied ML & ML Engineering in Microsoft Cloud Data Sciences in Cloud+AI. He has experience driving concept-completion-production ML projects, building out on-prem and on-the-cloud MLOps platforms, and conceptualizing and scaling extensible ML services. He has a track record of driving impact by incorporating ML into products & solutions.
 
Kiran has 40 filed and granted US patents. He is a Kaggle competition grandmaster (one of ~100 WW) with the highest WW rank of 7. He is a prize winner in the prestigious KDD Data Mining Cup. He is the recipient of the CTO award at VMware and the Innovator of the Year award from Michael Dell in person.

Enterprises are harnessing the power of Generative AI (GenAI) like never before. Buzzwords like ChatGPT, OpenAI, Gemini, and Co-pilot are everywhere. To meet high expectations and avoid another AI winter, it's crucial to deliver robust and reliable AI products.

This session starts with a brief overview of GenAI fundamentals to establish a common understanding of its capabilities and limitations. We will then dive into Retrieval-Augmented Generation (RAG) and its versatile applications across various business and technology sectors. We will discuss the challenges of moving from demos to real-world implementations, including the necessity of LLMOps. Real-world use cases will be presented to highlight these points. Additionally, we will cover Responsible AI to ensure ethical AI usage.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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